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Intelligent manufacturing production line data monitoring system for industrial internet of things
Journal Pre-proof Intelligent manufacturing production line data monitoring system for industrial internet of things Wei Chen
PII: DOI: Reference:
S0140-3664(19)31551-8 https://doi.org/10.1016/j.comcom.2019.12.035 COMCOM 6092
To appear in:
Computer Communications
Received date : 31 October 2019 Revised date : 4 December 2019 Accepted date : 19 December 2019 Please cite this article as: W. Chen, Intelligent manufacturing production line data monitoring system for industrial internet of things, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.12.035. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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Intelligent Manufacturing Production Line Data Monitoring System for Industrial Internet of Things Wei Chen School of Physics and Electrical Engineering, Weinan Normal University, Weinan 714000, Shaanxi Province, China *Corresponding author (Email: [email protected])
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Abstract
Applying the wireless sensor network of the Industrial Internet of Things and the radio frequency
identification technology to the production workshop of the discrete manufacturing industry, the real-
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time status of the shop floor can be automatically collected, providing a powerful decision-making basis for the upper-level planning management department. This paper proposes a reference architecture and construction path for smart factories by analyzing industrial IoT technology and its application in manufacturing workshops. Combined with the analysis of the status quo and needs of the discrete manufacturing enterprise workshop, this paper designs the overall architecture and
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theoretical model of the system. In view of the variety of on-site manufacturing data, large amount of data, variable status, heterogeneity, and strong correlation between data, integrated key technologies such as WSN and RFID, the industrial IoTs solution for manufacturing workshops is given. The multi-thread data real-time collection, storage technology and product tracking monitoring of the
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workshop are studied. Finally, the performance of the system is analyzed from the perspective of realtime and quality. The results show that the system is effective in the monitoring of production line data.
Keywords: intelligent manufacturing; industrial Internet of Things; production line data monitoring 1. Introduction
The manufacturing workshop is the core of the company's product production. The discrete manufacturing workshop is engaged in multi-variety and small-scale production, the production process is complex, the production scheduling is difficult, and the monitoring and management of the discrete manufacturing workshop has always been a problem that plagues the enterprise [1]. In the context of economic globalization, market competition has expanded from regional to global, and
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manufacturing is facing severe pressure to survive [2]. At the same time, customers' individualized demands are increasing, the uncertainty of survival orders is further enhanced, how to respond to diversified demands in a timely manner, adjusting the production process, shortening the production cycle and ensuring product quality, is the survival of enterprises. And the primary issue that needs to be addressed. However, opportunities and challenges coexist. The development of informatization and networking has brought tremendous impact and change to traditional business ideas and management methods, and also pointed out the direction for the development of manufacturing enterprises [3].
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Traditional order-oriented production methods are gradually shifting to service-oriented production methods [4]. Through information technology, enterprises can realize real-time control of the production process of the workshop, timely adjust production plans according to market changes or customer needs, improve market response speed, optimize manufacturing resources, improve production efficiency and reduce costs, and improve core competitiveness [5]. Intelligent
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manufacturing is the core of “Industry 4.0”, and manufacturing IoT technology is the foundation of intelligent manufacturing. It combines the technical means of IoT with the actual production of manufacturing to drive the production process and change the traditional workshop management
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mode. Through the identification of work-in-progress, dynamic tracking of the in-process product and real-time collection of production data can be realized, real-time detection of status information can be realized, and transparency of the manufacturing process of the shop can be improved [6]. In addition, the real-time positioning technology of the workshop has attracted more and more attention from the manufacturing enterprises. Through the real-time positioning of the production factors of the workshop, the position and state of each production factor are understood, the workshop distribution
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process is optimized, the object search time is reduced, and the comprehensive object of the workshop is realized [7].
Although the research on smart factories has just started, the definitions and ideas of smart factories have been proposed from different angles at home and abroad. Relevant scholars have made
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a specific definition of the smart factory [8,9]. They believe that the smart factory is based on the manufacturing of the object, through the data analysis to find the factory operating rules, using the rules to achieve intelligent decision-making, and then package the intelligent decision-making into intelligent services. Through the cloud agile configuration to achieve service synergy, a new product of the factory is formed by self-learning and self-adaptation [10]. Manufacturing IoT is a new manufacturing mode that combines IoT technology with manufacturing technology to realize the process of product manufacturing and service, as well as the dynamic perception, intelligent processing and optimal control of manufacturing resources and information resources throughout the product life cycle [11,12]. Some scholars have studied the application of RFID technology in the resource allocation of clothing manufacturing industry [13,14]. Through RFID real-time data, using fuzzy theory to deal with inaccurate information, combined with the characteristics of clothing manufacturing industry, they proposed a resource allocation system based on RFID to realize the
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optimization of manufacturing resource allocation. The application results in a clothing enterprise indicates that the system can optimize resource allocation [15]. Through the electronic identification of the item-level objects, the comprehensive collection of the quality data of the underlying items is realized, and the adaptive method of knowledge learning is used to realize the quality control of the manufacturing process [16]. Aiming at the dynamic production process of enterprises, some scholars put forward an RFID-based enterprise application integration framework, and gave a method to realize production dynamic management and work-in-process visualization under the framework, and
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verified the feasibility and reliability of the system [17,18]. In order to improve the efficiency of workshop monitoring, the researchers proposed a model-based workshop monitoring method, built a state compilation model through RFID observation variables, monitored production processes and anomalies based on event analysis and event processing techniques, and introduced Hidden Markov method for state diagnosis and prediction of the production process [19,20]. Related scholars studied
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the complex event construction method based on RFID raw data, defined the event by binary matrix, and considered the timing relationship between events, established a state monitoring model, realized the detection of interference events, and carried out case analysis, verifying the feasibility and
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effectiveness of monitoring [21,22]. Siemens designed a “digital factory” platform to divide the production process of the plant into several production nodes, track the manufacturing process information and data of each production node through real-time monitoring, and integrate and analyze the data for production. Process optimization, equipment fault diagnosis, MES (manufacturing execution system) and supply chain management have achieved the purpose of information interconnection from the lower floor production to the upper planning management department.
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This paper proposes a reference architecture for smart factories by studying the application of IoT technology in manufacturing workshops. In view of the shortcomings of the chaotic and robustness of most common manufacturing workshops, the extraction and monitoring of manufacturing process information is single and lagging, the system cost is high, and the upgrade and
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maintenance are difficult. The real-time tracking and monitoring system of intelligent workshop products based on the Internet of Things is proposed. The principle of IoT related technology and its implementation method in the workshop are analyzed. The related concepts of the existing smart factory are deeply analyzed, and the reference architecture and construction path of the smart factory are proposed. The industrial IoT system is the basis of the smart factory. The intelligent manufacturing workshop is a core component of the smart factory. Through the analysis of the status quo and demand of the workshops of discrete manufacturing enterprises in a certain area, the overall architecture and theoretical model of the system are designed, including the system functional structure model, system business process model and system architecture model. We designed and built the manufacturing workshop IoT, including the networking of equipment such as CNC machine tools in the workshop, wireless sensor network, radio frequency identification network, wireless control network and network integration, and proposed the method and principle of real-time data acquisition
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monitoring and product tracking in the workshop, providing hardware network foundation and technical theory support for the design of system software. The concept of the industrial Internet of things in the workshop was analyzed and explained. On this basis, the overall industrial IoT system of the intelligent workshop of the system was designed. The communication of communication terminals such as intelligent workshop CNC machine tools, intelligent workshop wireless sensor network and intelligent workshop radio frequency identification network are designed. The rest of this article is organized as follows. Section 2 discusses the industrial IoT technology
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for intelligent manufacturing, and Section 3 studies the overall design of the production line data monitoring system. Section 4 analyzes the functions of the workshop IoT and production line data monitoring system. Section 5 discusses the results of the production line real-time data monitoring system, and Section 6 summarizes the full text and gives the future research direction.
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2. Research on industrial IoT technology for intelligent manufacturing 2.1 Smart factory RFID technology
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RFID technology is a non-contact automatic identification technology that requires no human intervention. It consists of electronic tags, antennas, readers and application layer software. The electronic tag is bound to the item, and can store a small amount of coded data, generally adopting the methods of adhesion, printing, slot, etc., which is equivalent to the carrier of the product, and distributed among the manufacturing enterprises, the market, and the user. The tool for encoding data is connected to the transmission network and the client, and the related information is entered and
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updated through the application and the database, and the corresponding information of the product is obtained. The system database can be accessed through the Internet anywhere in the world, thereby achieving tracking of items and remote information query and management to achieve a global physical interconnection.
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The reader sends RF signals continuously at a certain frequency. When the electronic tag enters the read/write range of the reader, the electronic tag receives the RF signal sent by the reader and obtains energy through the induced current to issue the encoded information stored in the tag chip. Read by the reader, the reader receives and decodes the encoded information sent by the electronic tag, and then sends the decoded information to the PC application via the USB/RJ45/RS232/WIFI interface for corresponding processing.
RFID technology can read multiple tags at the same time, that is, multiple objects can be identified and read at the same time, which is suitable for situations where multiple entities share resources. The information storage capacity of the electronic tag chip is much larger than the previous bar code and can be set to read and write passwords, and the security is high. Choosing the right operating frequency is a very important step in RFID technology. Current RFID operating frequencies are broadly classified into low frequency systems, high frequency
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systems, and microwave systems. The data storage capacity, size, and read/write distance of the reader are also different for the different working frequencies. The higher the working frequency, the farther the recognition and reading distance is, the faster the speed, the larger the data storage capacity of the tag, and of course the higher the price. Since the power loss of the low frequency is proportional to the cube of the propagation distance, the power loss of the high frequency is proportional to the square of the propagation distance, so the high frequency can also be used for tracking and positioning the label. In the workshop, the state of the product undergoes a step-by-step process, the state is constantly
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changing, the position is constantly changing, and there is a large amount of dynamic information. Therefore, the application of RFID technology to the workshop, the electronic tag and the shop product are bound, not only can store the static information of the product in the whole life cycle, but also can read and write the real-time dynamic information of the product through the RFID reader without interruption. The physical topology of the discrete manufacturing workshop real-time
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monitoring system based on the industrial Internet of Things is shown in Figure 1.
Figure 1. Real-time monitoring system physical topology based on discrete manufacturing process of industrial Internet of Things
The system sets corresponding RFID readers and electronic billboards in each station in the workshop. The RFID readers collect real-time data in the production process. The current production
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tasks and production status can be transmitted to the workers through the electronic signage of the station. Each RFID reader is bound to the corresponding station, and sets the processed area and the area to be processed. When the production object with the electronic label enters the corresponding area, the data can be collected and according to the reader. The bound logical area gets its current location information.
2.2 Smart factory WSN technology
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The wireless sensor network integrates the sensor node module, the data conversion processing module and the data wireless transmission communication module, and is composed of a sensor interconnection system capable of not only data acquisition and processing but also wireless communication transmission. Through the cooperation of multiple integrated sensor nodes, the information to be tested in the monitoring area is collected and transmitted, thereby completing the
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collection, processing and analysis of information at any time in any place in the monitoring area. The wireless sensor network architecture generally consists of sensor nodes, aggregation nodes, and task management nodes. The sensor nodes are further divided into an anchor node and an
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unknown node according to the known location. The location known node is called an anchor node, and the location of the unknown node can be determined indirectly according to the distance between the anchor node and the anchor node.
The workshop has the characteristics of complex environment and many disturbances, and many environmental factors of the workshop, as well as the operating status and parameters of the equipment are the factors that affect the quality of the workshop products. By applying WSN
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technology to the workshop, not only can the sensor node idle, work status, intelligent scheduling, and dispatching of the sensor nodes be used to maximize the utilization of the equipment; it can also collect the displacement, temperature, and speed of the equipment during the manufacturing process in real time. The main operating parameter data that affects the processing quality, such as vibration,
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adjusts the operating parameters of the equipment in real time to ensure the quality of the product, so that the production equipment is always in the optimal energy efficiency state. According to the data collected for a long time, the coupling relationship between the various influencing factors during the operation of the equipment can be analyzed, and the variation rules of the operating parameters of the equipment can be summarized, thereby predicting the abnormal trend of the equipment, monitoring the equipment health condition and early warning of the fault. 2.3 Intelligent factory based on industrial internet of things The smart factory is in the "Internet +" environment, facing the development needs of digital, network and intelligent manufacturing. Through the integration of the Internet field and industrial manufacturing, we will promote the innovation of the manufacturing development model and the construction of an ecological modern industrial system. Germany's "Industry 4.0" and the "Industrial
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Interconnection" proposed by the United States are typical forms of smart factories. The reference architecture of the smart factory proposed in this paper is roughly summarized as the following four aspects.
1) Collaborative design and manufacturing process simulation optimization Based on cloud platform and big data driven resource scheduling and information design of service design, collaborative design can be realized. The simulation of manufacturing process based on virtual simulation technology is based on the relevant data of the whole life cycle of the product,
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modeling, simulation and control optimization of all production process flow such as workshop layout, equipment and manufacturing process. It builds complex industrial system models that integrate knowledge, data and models, and coordinates production and management elements, and links multidomain and multi-level knowledge. Based on real-time data production system parameters and state identification, it is a process simulation system for intelligent control of manufacturing equipment and
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production process. 2) Industrial Internet of things system
Industrial IoT system is an indispensable part of intelligent factory to achieve data collection,
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condition monitoring and information transmission, and provides support for intelligent monitoring and control of CPS manufacturing process. The industrial internet network system is shown in Figure
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2.
Figure 2. Industrial Internet of Things network architecture
3) Intelligent manufacturing workshop
The intelligent manufacturing workshop is a highly intelligent workshop based on the industrial Internet of Things, which automatically tracks and monitors the basic elements of workshop products and equipment, and intelligently controls the manufacturing process. The intelligent control of the
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manufacturing process automatically performs the decision-making process based on the decisionmaking knowledge base and manufacturing data by real-time storage extraction, analysis and processing of various manufacturing data such as tooling, process, and workpiece, as well as production equipment status and runtime parameters. Intelligent warehouse management is based on DFID, and realizes electronic and automated warehouses through electronic tags and stackers, making materials entering and leaving the warehouse, warehouse scheduling, and inventory management highly intelligent.
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4) Supply chain and operation service management Aiming at the needs of manufacturing supply chain resource integration, efficient management and service model innovation, a cloud-based supply value chain collaboration system supporting network manufacturing is established. Based on multi-system data fusion, through big data analysis and mining technology and artificial intelligence technology, the computer software automatically
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proposes optimization opinions on the operation, management and decision-making of the enterprise, and can obtain optimized results based on the comparison of the data before and after optimization.
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3. Research on the overall design of the production line data monitoring system 3.1 System requirements analysis
For the needs of enterprise management and actual production process, from the perspective of technical theory, it can be summarized into the following three functional requirements: (1) The networking of the production equipment, touch screen and other terminals in the workshop realizes
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real-time information transmission with the upper department; (2) Workshop equipment status, manufacturing process information and warehousing information are monitored in real time and presented in the management planning department with an intuitive software interface; (3) Product parts, standard parts, tooling, etc., should be monitored in real time.
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Therefore, based on the above functional requirements, an intelligent workshop production line data monitoring system based on industrial Internet of Things is designed, and the system needs to have the following performance: 1) Anti-interference
Due to the harsh environment in the workshop, the complicated lines, the complicated production process, the noise and heat generated by the machine, and the interference of many factors such as dust and electromagnetic field, it will have certain influence on the data transmission, especially for collecting digital signals and analog signals. In terms of frequency, it should have high antiinterference to overcome the interference factors in the harsh environment of the workshop, and ensure accurate and effective data collection and transmission. 2) Real-time
Real-time is the fundamental guarantee for enterprise production decision-making and control. If
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the real-time nature of the manufacturing process data of the equipment under test, product tracking and positioning data, etc. cannot be guaranteed, then the authenticity of the data at the determined moment is lost, and the control and scheduling of the workshop is meaningless, which will result in a series of problems. Moreover, due to the large number of monitoring units and parameters, and all parameters need to be displayed in real time, it is necessary to use multi-threading to ensure the realtime performance of all data. 3) Rapid deployment capability
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Due to the large number of equipment types in the workshop and the different operating systems, there is a demand for cross-platform and compatible integration capabilities of the system to achieve rapid deployment and cost savings. 4) Convenient expansion As enterprises are constantly developing, their business and needs are not always the same,
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which requires the system to be easily expanded and upgraded to adapt to the business changes and evolving needs of the enterprise. 5) Security
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Security is a problem that must be considered by any industry. The main security issues considered by this system are to prevent external attacks from causing data leakage or tampering, and on the other hand, data or program loss caused by hardware device failure or improper operation by the workshop. Data storage exceptions are caused when parallel data volumes are overloaded, which
3.2 System function structure model
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imposes requirements on the system's architecture, security policies, and ability to handle exceptions.
The overall functional structure model of the system is divided into six modules: system login, DNC, workshop monitoring, warehouse management, product tracking, and statistical analysis. Each main function module includes multiple sub-function modules, as follows:
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1) System login module
The module mainly includes four sub-function modules: user registration, user login, password management and rights management, to realize the security protection of the system, and open different corresponding modules for users with different positions in different departments to view the modification authority and data security for the enterprise. 2) DNC module
The module mainly includes five sub-function modules: process document management, NC equipment management, NC code transmission, drawing model transmission, and process card transmission, so as to establish serial port and network port communication with the communication machine such as CNC machine tools, equipment and touch screens in the workshop. The device sends and receives process documents such as NC codes, process cards, 2D drawings, 3D models, and the management of these documents.
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3) Workshop monitoring module
The module is mainly for the monitoring of each workshop, real-time extraction of the detailed manufacturing process data such as the running status, operating parameters, current machining parts, production task completion of each workshop, and real-time adjustment equipment operating parameters, monitoring equipment health status, and according to this, it carries out on-demand distribution of production tasks, fault diagnosis, maintenance alarms and other actions. 4) Warehouse management module
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The module mainly includes four sub-function modules: RFID device management, storage management, outbound management, and inventory management, to realize RFID reader parameter and mode setting, electronic label initialization, product storage coding and information entry. 5) Product tracking module The module mainly includes two sub-function modules: product real-time status tracking and
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product real-time status query. The tracking and query information includes: basic product information, current location information of the product, current process information of the product, current processing equipment information of the product, current processing progress of the product,
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and current responsible information of the product. 6) Statistical analysis module
The module mainly includes: equipment utilization statistics, equipment failure rate statistics, product qualification rate statistics, product failure reason statistics, and workshop production statistics. It generates statistical analysis charts or reports to provide a reliable data basis for and equipment maintenance. 3.3 System architecture model
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management analysis departments to make reasonable planning decisions, rewards and punishments,
The client/server mode is the mainstream choice of the current computing mode. The client
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passes the data to the server. The server analyzes the data passed and performs related operations on the database according to the analysis result, and then returns the result to the client. The C/S mode has the characteristics of strong interactivity, safe data storage mode, short response time, low communication volume, and good processing of large amounts of data. Due to the large amount of data interaction in this system, the data needs multi-thread real-time acquisition, and requires high real-time interactive responsiveness and data security. Therefore, the classic C/S architecture system is adopted here, and the computer system and workshop are connected through the workshop Internet of Things. CNC equipment such as communication terminals, RFID readers, sensors and other hardware devices are connected, and the corresponding client is installed on each terminal client of the workshop, and real-time data information is collected and stored in the remote database server for the whole system call.
These factors are taken into account: (1) The harsh environment of the workshop and the
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complexity of the lines will have a certain impact on network cabling and data signal transmission. Therefore, the wireless connection is used for the whole workshop network, and the AP client access point client mode is selected. Under the premise of ensuring signal strength, the wireless AP is used to extend the coverage of the network to the entire workshop to realize wireless transmission of the network; (2) Considering the security of the network and data, all the workshops of the Internet of Things are integrated into the workshop Ethernet, and then connected to the corporate Internet through the firewall; (3) Each terminal device is coded by IP address in the workshop LAN, and each
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terminal device is bound with a fixed IP address to achieve accurate command and data transmission. The system architecture model is shown in Figure 3. In the workshop, the Internet of Things first needs to connect the communication terminals such as CNC machine tools and touch screens in the workshop to realize real-time communication between the upper design management department and the workshop, transmission of NC code to CNC machine tools, transmission of process documents,
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and release of tasks. Secondly, the sensors and RFID devices arranged in the workshop need to be networked, and the real-time data, positioning information and real-time data of the workshop production are converted into wireless network collection data acquisition host through the sensors
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and RFID readers arranged in the workshop, and then through the workshop. Ethernet uploads and saves data to the real-time database server for office area retrieval and visual real-time monitoring. Then, it is the networking of the execution terminal of the workshop. The workshop control center makes management and scheduling decisions according to the real-time production data of the workshop, and sends control commands to the integrated controller through the wireless network to control the intelligent execution units of the workshop. Finally, all the networks are integrated into the
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and data security.
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workshop Ethernet, and the enterprise Internet is connected through the firewall to ensure network
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Figure 3. System architecture model 4. Workshop internet of things and production line data monitoring system function research 4.1 Networking design of CNC machine tools and touch screens in the workshop
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In order to realize the real-time transmission of manufacturing data to the workshop, the NC code is transmitted to the CNC machine tool, and the process card, 2D drawing, 3D model, etc. are transmitted to the station touch screen. The working status of the CNC machine tool and the
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production data are uploaded to the office area in real time. However, the brands and models of CNC machine tools used by various enterprises are different. Due to the updating and introduction of machine tools, there are various types of CNC machine tools in different workshops, which leads to inconsistencies in the reserved network interfaces. At present, the reserved networking interface of CNC machine tools in China is generally RS232, RS485 serial interface and RJ45 Ethernet port. Therefore, for the above serial interface and network port, the networking scheme of workshop CNC
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machine tool and station touch screen is designed as shown in Figure 4.
Figure 4. Intelligent workshop CNC machine tool and workstation touch screen networking solution Due to the complex environment in the workshop, the numerous lines, and the distance from the office area, the wire line should be minimized, the line anti-interference ability should be enhanced, and the serial transmission distance should be overcome. Here, the RS232 and RS485 serial ports are converted into RJ45 Ethernet ports through the serial port server, and then connected to the wireless
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access device to be converted into a wireless network. The wireless connector is powered by the Passive Po E network cable, so that the AP can get rid of the limitation of the power access point, so that the AP The wireless access device is directly pasted on the CNC machine tool, and then wirelessly connected with other APs through the AP client access point client mode of the wireless access device, and all the machine tools and touch screens in the workshop are set to a fixed IP
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address and a MAK address. In the local area network between the established CNC machine tool and the server, the communication and file interaction are realized by accessing the shared files under the respective IP
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addresses. The network module of the machine tool is similar to the working mode of the PC network card, and consists of hardware and software. Hardware refers to the network card and network card driver, and the software is the network communication module of the machine tool. 4.2 Design and function realization of intelligent workshop wireless sensor network 1) Design of workshop wireless sensor network
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In view of the complex environment and high interference of the workshop, the wireless sensor network of the workshop needs to arrange the sensor nodes reasonably, and collect the real-time working state of the equipment through the sensor to collect the signal light of the equipment, the magnetic flux of the motor or directly collect by the PLC, through different sensors.
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The sensor nodes arranged in the workshop continuously transmit the digital and analog signals to the coordinator. The coordinator transmits the signals to the processor for AD conversion and signal amplification through the serial port asynchronous communication, and then converts the signals into wireless signals through the wireless network card. The wireless monitoring video signal transmitted by the high-definition camera is sent to the wireless sensing gateway together, and finally enters the workshop local area network and is stored in the database server, and the application terminal such as the control center converts the collected voltage, current and other signals into corresponding collection parameter values through a calculation program. The design of the workshop wireless
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sensor network reference layout scheme is shown in Figure 5.
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Figure 5. Workshop wireless sensor network design architecture 2) Design of workshop status monitoring function The wireless sensor network communicates with many sensor nodes arranged in the monitoring area and self-organizes through the aggregation node, and finally transmits the collected data
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information to the remote task management node for related processing. The sensor node usually consists of the following four parts:
The sensor module is used to collect various information of the object to be collected in the
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monitoring area and convert the collected data into a format convenient for transmission. The processor module is used to store and process the data collected by the sensor module. The wireless communication module is configured to convert the processed data signal into a wireless signal, and exchange control information with other sensor nodes, and send and receive data. The energy supply module is used to energize the sensor nodes to enable them to function properly.
After the above sensor nodes are powered on, the parameters are initialized, the input signal is
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converted by AD, the corresponding channel is selected, and the data information collected by the sensor is sent to the coordinator according to the set frequency, and then transmitted to the processor for processing according to the asynchronous serial communication protocol. After that, it is converted into a wireless signal through a wireless network card, and is connected to the workshop
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LAN and the Wi Fi/GPRS network, and finally the collected manufacturing data is displayed in the smart phone terminal and the control center.
4.3 Design and function realization of the network in the workshop radio frequency 1) Design of workshop radio frequency identification network The intelligent workshop radio frequency identification network is established, so that all entities in the workshop have their own unique identification, and their attributes, processes and locations can be queried at any time. This provides great convenience to the management. On the one hand, it can track and query the processing progress of WIP at any time. The time required for each process is stagnation, and the cause is found in time and intelligently dispatched. On the other hand, it can be tracked, avoiding the problems of manual registration in traditional workshops, difficulty in finding tools and tooling. The smart factory radio frequency identification network reference architecture is
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shown in Figure 6.
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Figure 6. Smart factory manufacturing workshop radio frequency identification network design architecture
The architecture generally follows the IoT general architecture system of the sensing layer, the network layer, and the application layer. The bottom-up is an RFID tag that corresponds to the entity object, and the static data is stored, and the RFID reader perceives the entity object. 2) Intelligent production workshop production line data monitoring function design The communication process of the RFID application system is that the reader/writer module is connected with the host computer via the USB/RJ45/RS232/WIFI interface, and receives and executes the commands sent by the host computer one by one, and finally returns the executed result information to the upper computer.
When the host computer sends the command data block to the RFID reader module, the interval between adjacent characters must be less than 12ms. Otherwise, the previously sent data will be
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directly discarded by the RFID reader module, and the data after 12ms will be treated as a new one. The command data block is re-received until the read-write module receives the correct command, executes the command, and returns a response to the reader module. The process of sending the response data by the reader/writer is actually the process of returning the execution result and the response data to the upper computer after the reader receives and executes the command sent by the upper computer, which is a complete communication process. The memory of the electronic tag can be logically divided into four memory areas, each of which typically contains
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one or more memory words. The four storage areas are an EPC area, a TID area, a user area, and a reserved area, respectively. Each product is attached to the warehouse with an electronic label, and the product code and other information is written into the electronic label storage area, and the relevant information is entered into the database. The product code finds the unique product information of the database. By
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arranging the corresponding readers and configuring the client program at the appropriate position in the workshop, the principle of “one read, one end and multiple bits” is followed. When the product passes through the reader, it will be automatically recognized, and the current location, current state,
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time and other information will be written into the electronic tag storage area according to the fixed encoding format or the database data will be updated in real time according to the number, as shown in Figure 7, thus realizing the real-time tracking query for the workshop product.
Lathe area
Data input Electronic label 2
Reader 2 (lathe 2)
Electronic label 4
Write and update the database
Return data
Query command
Reader 3 (lathe 3)
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Database server
Client
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Read tag encoding Electronic label 3
Reader 1 (lathe 1)
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Electronic label 1
Milling area
Figure 7. Using RFID technology to implement product monitoring methods 5. Production line real-time data monitoring system results analysis 5.1 Real-time analysis
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This section analyzes and studies the operation results of product-based data acquisition and monitoring systems from the aspects of hardware and software execution efficiency and production efficiency before and after system implementation. On the hardware, Line Server runs on high-performance servers, and its processing speed is higher than that of ordinary computers or industrial computers. In terms of data and real-time signal processing, its speed can meet the requirements of industrial sites. In software design, Line Server uses a linked list to manage the connection with each device, and scans the common monitoring bits
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of each station every 480 milliseconds. In the database query speed, the database is installed locally. In terms of network, since the PLC and the Line Server are directly connected through the Ethernet and in the same LAN segment, the speed of the network transmission is related to the performance of the switch, and the transmission delay through the system tester is less than 90 microseconds. By counting the running time markers of the PLC, the machining cycle of each station includes
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an online machining cycle and an offline machining cycle. The online machining cycle process includes: the workpiece reaches the fixture position, the feedback of the confirmation signal, the device startup processing, the upload of the processing data, and the device reset. There are no data
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acquisition and control signals for offline processing.
The time consuming of the data acquisition system and the control system is the difference between the time taken by the line processing cycle and the time taken by the offline processing cycle. Table 1 records the online and offline processing time statistics for a certain month. The timeconsuming fluctuations of different stations are relatively large, which is related to the process complexity of different stations. Here, the relative size of the system time-consuming and offline
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processing time is mainly analyzed. Figure 8 shows the online machining cycle, offline machining cycle, and average time consumption. The longer the line passes through the x-axis, the shorter the time it takes for the data acquisition and control system.
Functional average time consuming Offline processing cycle Online processing cycle
20
Time
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15 10 5 0
1
2
3
4
5
6
7
8
9 10 11
Station
3 1 2 Cycle and time consumption
Figure 8. Online and offline processing cycle diagram of each station in a month Table 1. Average processing time of online and offline for each station in each month Station (OP
Offline
Functional
Production
Production
processing
processing
average time
line MES
line MES
cycle (s)
cycle (s)
consuming (s)
function
function
average time
overall time
consuming (s)
consuming (s)
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No)
Online
12
14.10
13.10
0.80
-
-
21
12.50
11.40
0.70
-
-
32
9.40
8.50
0.50
-
-
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8.20
7.20
0.80
-
-
54
10.10
9.10
0.70
-
-
61
16.90
16.30
0.80
-
-
72
6.80
6.20
0.90
0.42
5.00
83
0.90
0.80
0
-
-
94
0
0
0
-
-
102
19.70
18.80
0.60
113
0.08
0.10
0
121
14.70
13.70
0.10
134
20.20
20.10
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40
-
-
-
-
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-
0.10
-
-
After statistical analysis, the average time-consuming data acquisition and monitoring system of the single line of the production line is 0.42 seconds, which can meet the real-time requirements of the
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automation site. The entire production line completes one workpiece. The average time taken after implementing the system is about 127.6 seconds. The execution time of data acquisition and control is 5.4 seconds, which accounts for 4.10% of the processing time of the entire production line. It has basically no effect on the completion of the production plan.
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5.2 Quality control analysis
Key indicators of production process control of enterprises KPIs include rework rate, defective rate, yield rate, scrap rate and other indicators, which is an indicator to evaluate the comprehensive production efficiency of production lines. The equipment will verify its state before producing a single workpiece, and will remind the workpiece that is not qualified or produced according to the process requirements, and stop production, thus eliminating the non-conforming product and the workpiece that does not follow the process flow into the next process. The rework data table counts the number of rework per station.
The production process of this production line is complicated, the work station is more, the process is crossed, and the rework operations of different stations are different. For example, the OP90 is pre-installed for the bracket, there is no rework operation, and rework is not possible. The pre-installation failure is directly scrapped, and the OP70 is reworked. By configuring in the
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configuration interface, the workpiece will automatically reach the rework station after entering the production line again. Other stations will skip or prohibit production, the unconfigured workpiece can only be reworked in the current process. The number of times of rework can also be limited according to the relevant configuration to ensure the orderly production and process controllability. Table 2 counts the number of unqualified stations in a certain month. The total output of the production line in the month is 31217, of which 30752 is qualified, the number of workpiece rework
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is 177, and the number of processing scraps is 916. In the process of scrapping, the terminal inspection was 142 times, accounting for 14.15% of the total scrap rate. The scrap rate for the month was 2.79%. When the data collection and control system was not deployed, the average scrap rate was 3.22%, the end-of-line scrap rate was 74.39%, and the scrap rate was reduced by about 18%. The ratio
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of scrapped at the terminal was reduced. 76%. Table 2. Statistics of rework/unqualified times of each station in a month Unqualified (times)
12
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Station (OP No) 21
0
32
8
40
12
54
2
72 83 94 102
6. Conclusion
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0
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61
5
64 64 152 6
121
42
134
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This paper analyzes the related concepts of the existing smart factory and proposes the reference architecture and construction path of the smart factory. The industrial IoT system is the foundation of smart factories, and the intelligent manufacturing workshops make the core components of smart factories. Through the analysis of the related technologies of IoT and its implementation methods in the workshop, the core technologies of two IoT technologies, such as radio frequency identification and wireless sensor networks, are applied to design the industrial Internet of the workshop. The realtime tracking of the manufacturing workshop products and the real-time monitoring of the workshop
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status and the workshop equipment status are realized, which proves the correctness and feasibility of the research theory and key technologies. However, real-time monitoring of the manufacturing process is only the first step toward intelligent manufacturing. To achieve intelligent production, production factors need to have the ability of self-identification, intelligent perception, independent decision-making and knowledge learning. This requires further exploration in the areas of manufacturing IoT technology, artificial intelligence algorithms, and machine learning. In the future,
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the system can be combined with cloud service technology to hang each functional module in Yunping. The platform is engaged in leasing services, and each company rents its own modules on the cloud platform according to their respective needs and applies them to actual enterprises.
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Acknowledgement
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This work is supported by the Shaanxi Military and Civilian Integration Research Fund Project, Research of PID Controller Applied to Pubai Xigu Coal Mine Gas Drainage System(18JMR40); Project of Weinan Normal University, Research on Tuning Methods of PID Controller Parameters used in Process Control System(7YKS06).
References
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Wei Chen was born in Henan, China, in 1984. She received the BS degree from China University of Mining & Technology, Xuzhou, China, in 2006, and the ME degree from China University of Mining & Technology, Beijing, China in 2010. After graduation she worked as a teacher in School of Physics and Electrical Engineering at Weinan Normal University since 2010. She has got a further study in Shaanxi Normal University in 2017. She has published 12 papers and holds 3 patents. Her research interests include wireless sensor networks, and advanced control theory. Email: [email protected]
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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Wei Chen
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Conceptualization, Methodology, Writing- Original draft preparation.
PII: DOI: Reference:
S0140-3664(19)31551-8 https://doi.org/10.1016/j.comcom.2019.12.035 COMCOM 6092
To appear in:
Computer Communications
Received date : 31 October 2019 Revised date : 4 December 2019 Accepted date : 19 December 2019 Please cite this article as: W. Chen, Intelligent manufacturing production line data monitoring system for industrial internet of things, Computer Communications (2019), doi: https://doi.org/10.1016/j.comcom.2019.12.035. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
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Intelligent Manufacturing Production Line Data Monitoring System for Industrial Internet of Things Wei Chen School of Physics and Electrical Engineering, Weinan Normal University, Weinan 714000, Shaanxi Province, China *Corresponding author (Email: [email protected])
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Abstract
Applying the wireless sensor network of the Industrial Internet of Things and the radio frequency
identification technology to the production workshop of the discrete manufacturing industry, the real-
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time status of the shop floor can be automatically collected, providing a powerful decision-making basis for the upper-level planning management department. This paper proposes a reference architecture and construction path for smart factories by analyzing industrial IoT technology and its application in manufacturing workshops. Combined with the analysis of the status quo and needs of the discrete manufacturing enterprise workshop, this paper designs the overall architecture and
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theoretical model of the system. In view of the variety of on-site manufacturing data, large amount of data, variable status, heterogeneity, and strong correlation between data, integrated key technologies such as WSN and RFID, the industrial IoTs solution for manufacturing workshops is given. The multi-thread data real-time collection, storage technology and product tracking monitoring of the
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workshop are studied. Finally, the performance of the system is analyzed from the perspective of realtime and quality. The results show that the system is effective in the monitoring of production line data.
Keywords: intelligent manufacturing; industrial Internet of Things; production line data monitoring 1. Introduction
The manufacturing workshop is the core of the company's product production. The discrete manufacturing workshop is engaged in multi-variety and small-scale production, the production process is complex, the production scheduling is difficult, and the monitoring and management of the discrete manufacturing workshop has always been a problem that plagues the enterprise [1]. In the context of economic globalization, market competition has expanded from regional to global, and
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manufacturing is facing severe pressure to survive [2]. At the same time, customers' individualized demands are increasing, the uncertainty of survival orders is further enhanced, how to respond to diversified demands in a timely manner, adjusting the production process, shortening the production cycle and ensuring product quality, is the survival of enterprises. And the primary issue that needs to be addressed. However, opportunities and challenges coexist. The development of informatization and networking has brought tremendous impact and change to traditional business ideas and management methods, and also pointed out the direction for the development of manufacturing enterprises [3].
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Traditional order-oriented production methods are gradually shifting to service-oriented production methods [4]. Through information technology, enterprises can realize real-time control of the production process of the workshop, timely adjust production plans according to market changes or customer needs, improve market response speed, optimize manufacturing resources, improve production efficiency and reduce costs, and improve core competitiveness [5]. Intelligent
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manufacturing is the core of “Industry 4.0”, and manufacturing IoT technology is the foundation of intelligent manufacturing. It combines the technical means of IoT with the actual production of manufacturing to drive the production process and change the traditional workshop management
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mode. Through the identification of work-in-progress, dynamic tracking of the in-process product and real-time collection of production data can be realized, real-time detection of status information can be realized, and transparency of the manufacturing process of the shop can be improved [6]. In addition, the real-time positioning technology of the workshop has attracted more and more attention from the manufacturing enterprises. Through the real-time positioning of the production factors of the workshop, the position and state of each production factor are understood, the workshop distribution
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process is optimized, the object search time is reduced, and the comprehensive object of the workshop is realized [7].
Although the research on smart factories has just started, the definitions and ideas of smart factories have been proposed from different angles at home and abroad. Relevant scholars have made
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a specific definition of the smart factory [8,9]. They believe that the smart factory is based on the manufacturing of the object, through the data analysis to find the factory operating rules, using the rules to achieve intelligent decision-making, and then package the intelligent decision-making into intelligent services. Through the cloud agile configuration to achieve service synergy, a new product of the factory is formed by self-learning and self-adaptation [10]. Manufacturing IoT is a new manufacturing mode that combines IoT technology with manufacturing technology to realize the process of product manufacturing and service, as well as the dynamic perception, intelligent processing and optimal control of manufacturing resources and information resources throughout the product life cycle [11,12]. Some scholars have studied the application of RFID technology in the resource allocation of clothing manufacturing industry [13,14]. Through RFID real-time data, using fuzzy theory to deal with inaccurate information, combined with the characteristics of clothing manufacturing industry, they proposed a resource allocation system based on RFID to realize the
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optimization of manufacturing resource allocation. The application results in a clothing enterprise indicates that the system can optimize resource allocation [15]. Through the electronic identification of the item-level objects, the comprehensive collection of the quality data of the underlying items is realized, and the adaptive method of knowledge learning is used to realize the quality control of the manufacturing process [16]. Aiming at the dynamic production process of enterprises, some scholars put forward an RFID-based enterprise application integration framework, and gave a method to realize production dynamic management and work-in-process visualization under the framework, and
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verified the feasibility and reliability of the system [17,18]. In order to improve the efficiency of workshop monitoring, the researchers proposed a model-based workshop monitoring method, built a state compilation model through RFID observation variables, monitored production processes and anomalies based on event analysis and event processing techniques, and introduced Hidden Markov method for state diagnosis and prediction of the production process [19,20]. Related scholars studied
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the complex event construction method based on RFID raw data, defined the event by binary matrix, and considered the timing relationship between events, established a state monitoring model, realized the detection of interference events, and carried out case analysis, verifying the feasibility and
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effectiveness of monitoring [21,22]. Siemens designed a “digital factory” platform to divide the production process of the plant into several production nodes, track the manufacturing process information and data of each production node through real-time monitoring, and integrate and analyze the data for production. Process optimization, equipment fault diagnosis, MES (manufacturing execution system) and supply chain management have achieved the purpose of information interconnection from the lower floor production to the upper planning management department.
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This paper proposes a reference architecture for smart factories by studying the application of IoT technology in manufacturing workshops. In view of the shortcomings of the chaotic and robustness of most common manufacturing workshops, the extraction and monitoring of manufacturing process information is single and lagging, the system cost is high, and the upgrade and
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maintenance are difficult. The real-time tracking and monitoring system of intelligent workshop products based on the Internet of Things is proposed. The principle of IoT related technology and its implementation method in the workshop are analyzed. The related concepts of the existing smart factory are deeply analyzed, and the reference architecture and construction path of the smart factory are proposed. The industrial IoT system is the basis of the smart factory. The intelligent manufacturing workshop is a core component of the smart factory. Through the analysis of the status quo and demand of the workshops of discrete manufacturing enterprises in a certain area, the overall architecture and theoretical model of the system are designed, including the system functional structure model, system business process model and system architecture model. We designed and built the manufacturing workshop IoT, including the networking of equipment such as CNC machine tools in the workshop, wireless sensor network, radio frequency identification network, wireless control network and network integration, and proposed the method and principle of real-time data acquisition
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monitoring and product tracking in the workshop, providing hardware network foundation and technical theory support for the design of system software. The concept of the industrial Internet of things in the workshop was analyzed and explained. On this basis, the overall industrial IoT system of the intelligent workshop of the system was designed. The communication of communication terminals such as intelligent workshop CNC machine tools, intelligent workshop wireless sensor network and intelligent workshop radio frequency identification network are designed. The rest of this article is organized as follows. Section 2 discusses the industrial IoT technology
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for intelligent manufacturing, and Section 3 studies the overall design of the production line data monitoring system. Section 4 analyzes the functions of the workshop IoT and production line data monitoring system. Section 5 discusses the results of the production line real-time data monitoring system, and Section 6 summarizes the full text and gives the future research direction.
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2. Research on industrial IoT technology for intelligent manufacturing 2.1 Smart factory RFID technology
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RFID technology is a non-contact automatic identification technology that requires no human intervention. It consists of electronic tags, antennas, readers and application layer software. The electronic tag is bound to the item, and can store a small amount of coded data, generally adopting the methods of adhesion, printing, slot, etc., which is equivalent to the carrier of the product, and distributed among the manufacturing enterprises, the market, and the user. The tool for encoding data is connected to the transmission network and the client, and the related information is entered and
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updated through the application and the database, and the corresponding information of the product is obtained. The system database can be accessed through the Internet anywhere in the world, thereby achieving tracking of items and remote information query and management to achieve a global physical interconnection.
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The reader sends RF signals continuously at a certain frequency. When the electronic tag enters the read/write range of the reader, the electronic tag receives the RF signal sent by the reader and obtains energy through the induced current to issue the encoded information stored in the tag chip. Read by the reader, the reader receives and decodes the encoded information sent by the electronic tag, and then sends the decoded information to the PC application via the USB/RJ45/RS232/WIFI interface for corresponding processing.
RFID technology can read multiple tags at the same time, that is, multiple objects can be identified and read at the same time, which is suitable for situations where multiple entities share resources. The information storage capacity of the electronic tag chip is much larger than the previous bar code and can be set to read and write passwords, and the security is high. Choosing the right operating frequency is a very important step in RFID technology. Current RFID operating frequencies are broadly classified into low frequency systems, high frequency
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systems, and microwave systems. The data storage capacity, size, and read/write distance of the reader are also different for the different working frequencies. The higher the working frequency, the farther the recognition and reading distance is, the faster the speed, the larger the data storage capacity of the tag, and of course the higher the price. Since the power loss of the low frequency is proportional to the cube of the propagation distance, the power loss of the high frequency is proportional to the square of the propagation distance, so the high frequency can also be used for tracking and positioning the label. In the workshop, the state of the product undergoes a step-by-step process, the state is constantly
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changing, the position is constantly changing, and there is a large amount of dynamic information. Therefore, the application of RFID technology to the workshop, the electronic tag and the shop product are bound, not only can store the static information of the product in the whole life cycle, but also can read and write the real-time dynamic information of the product through the RFID reader without interruption. The physical topology of the discrete manufacturing workshop real-time
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monitoring system based on the industrial Internet of Things is shown in Figure 1.
Figure 1. Real-time monitoring system physical topology based on discrete manufacturing process of industrial Internet of Things
The system sets corresponding RFID readers and electronic billboards in each station in the workshop. The RFID readers collect real-time data in the production process. The current production
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tasks and production status can be transmitted to the workers through the electronic signage of the station. Each RFID reader is bound to the corresponding station, and sets the processed area and the area to be processed. When the production object with the electronic label enters the corresponding area, the data can be collected and according to the reader. The bound logical area gets its current location information.
2.2 Smart factory WSN technology
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The wireless sensor network integrates the sensor node module, the data conversion processing module and the data wireless transmission communication module, and is composed of a sensor interconnection system capable of not only data acquisition and processing but also wireless communication transmission. Through the cooperation of multiple integrated sensor nodes, the information to be tested in the monitoring area is collected and transmitted, thereby completing the
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collection, processing and analysis of information at any time in any place in the monitoring area. The wireless sensor network architecture generally consists of sensor nodes, aggregation nodes, and task management nodes. The sensor nodes are further divided into an anchor node and an
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unknown node according to the known location. The location known node is called an anchor node, and the location of the unknown node can be determined indirectly according to the distance between the anchor node and the anchor node.
The workshop has the characteristics of complex environment and many disturbances, and many environmental factors of the workshop, as well as the operating status and parameters of the equipment are the factors that affect the quality of the workshop products. By applying WSN
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technology to the workshop, not only can the sensor node idle, work status, intelligent scheduling, and dispatching of the sensor nodes be used to maximize the utilization of the equipment; it can also collect the displacement, temperature, and speed of the equipment during the manufacturing process in real time. The main operating parameter data that affects the processing quality, such as vibration,
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adjusts the operating parameters of the equipment in real time to ensure the quality of the product, so that the production equipment is always in the optimal energy efficiency state. According to the data collected for a long time, the coupling relationship between the various influencing factors during the operation of the equipment can be analyzed, and the variation rules of the operating parameters of the equipment can be summarized, thereby predicting the abnormal trend of the equipment, monitoring the equipment health condition and early warning of the fault. 2.3 Intelligent factory based on industrial internet of things The smart factory is in the "Internet +" environment, facing the development needs of digital, network and intelligent manufacturing. Through the integration of the Internet field and industrial manufacturing, we will promote the innovation of the manufacturing development model and the construction of an ecological modern industrial system. Germany's "Industry 4.0" and the "Industrial
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Interconnection" proposed by the United States are typical forms of smart factories. The reference architecture of the smart factory proposed in this paper is roughly summarized as the following four aspects.
1) Collaborative design and manufacturing process simulation optimization Based on cloud platform and big data driven resource scheduling and information design of service design, collaborative design can be realized. The simulation of manufacturing process based on virtual simulation technology is based on the relevant data of the whole life cycle of the product,
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modeling, simulation and control optimization of all production process flow such as workshop layout, equipment and manufacturing process. It builds complex industrial system models that integrate knowledge, data and models, and coordinates production and management elements, and links multidomain and multi-level knowledge. Based on real-time data production system parameters and state identification, it is a process simulation system for intelligent control of manufacturing equipment and
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production process. 2) Industrial Internet of things system
Industrial IoT system is an indispensable part of intelligent factory to achieve data collection,
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condition monitoring and information transmission, and provides support for intelligent monitoring and control of CPS manufacturing process. The industrial internet network system is shown in Figure
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2.
Figure 2. Industrial Internet of Things network architecture
3) Intelligent manufacturing workshop
The intelligent manufacturing workshop is a highly intelligent workshop based on the industrial Internet of Things, which automatically tracks and monitors the basic elements of workshop products and equipment, and intelligently controls the manufacturing process. The intelligent control of the
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manufacturing process automatically performs the decision-making process based on the decisionmaking knowledge base and manufacturing data by real-time storage extraction, analysis and processing of various manufacturing data such as tooling, process, and workpiece, as well as production equipment status and runtime parameters. Intelligent warehouse management is based on DFID, and realizes electronic and automated warehouses through electronic tags and stackers, making materials entering and leaving the warehouse, warehouse scheduling, and inventory management highly intelligent.
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4) Supply chain and operation service management Aiming at the needs of manufacturing supply chain resource integration, efficient management and service model innovation, a cloud-based supply value chain collaboration system supporting network manufacturing is established. Based on multi-system data fusion, through big data analysis and mining technology and artificial intelligence technology, the computer software automatically
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proposes optimization opinions on the operation, management and decision-making of the enterprise, and can obtain optimized results based on the comparison of the data before and after optimization.
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3. Research on the overall design of the production line data monitoring system 3.1 System requirements analysis
For the needs of enterprise management and actual production process, from the perspective of technical theory, it can be summarized into the following three functional requirements: (1) The networking of the production equipment, touch screen and other terminals in the workshop realizes
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real-time information transmission with the upper department; (2) Workshop equipment status, manufacturing process information and warehousing information are monitored in real time and presented in the management planning department with an intuitive software interface; (3) Product parts, standard parts, tooling, etc., should be monitored in real time.
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Therefore, based on the above functional requirements, an intelligent workshop production line data monitoring system based on industrial Internet of Things is designed, and the system needs to have the following performance: 1) Anti-interference
Due to the harsh environment in the workshop, the complicated lines, the complicated production process, the noise and heat generated by the machine, and the interference of many factors such as dust and electromagnetic field, it will have certain influence on the data transmission, especially for collecting digital signals and analog signals. In terms of frequency, it should have high antiinterference to overcome the interference factors in the harsh environment of the workshop, and ensure accurate and effective data collection and transmission. 2) Real-time
Real-time is the fundamental guarantee for enterprise production decision-making and control. If
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the real-time nature of the manufacturing process data of the equipment under test, product tracking and positioning data, etc. cannot be guaranteed, then the authenticity of the data at the determined moment is lost, and the control and scheduling of the workshop is meaningless, which will result in a series of problems. Moreover, due to the large number of monitoring units and parameters, and all parameters need to be displayed in real time, it is necessary to use multi-threading to ensure the realtime performance of all data. 3) Rapid deployment capability
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Due to the large number of equipment types in the workshop and the different operating systems, there is a demand for cross-platform and compatible integration capabilities of the system to achieve rapid deployment and cost savings. 4) Convenient expansion As enterprises are constantly developing, their business and needs are not always the same,
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which requires the system to be easily expanded and upgraded to adapt to the business changes and evolving needs of the enterprise. 5) Security
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Security is a problem that must be considered by any industry. The main security issues considered by this system are to prevent external attacks from causing data leakage or tampering, and on the other hand, data or program loss caused by hardware device failure or improper operation by the workshop. Data storage exceptions are caused when parallel data volumes are overloaded, which
3.2 System function structure model
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imposes requirements on the system's architecture, security policies, and ability to handle exceptions.
The overall functional structure model of the system is divided into six modules: system login, DNC, workshop monitoring, warehouse management, product tracking, and statistical analysis. Each main function module includes multiple sub-function modules, as follows:
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1) System login module
The module mainly includes four sub-function modules: user registration, user login, password management and rights management, to realize the security protection of the system, and open different corresponding modules for users with different positions in different departments to view the modification authority and data security for the enterprise. 2) DNC module
The module mainly includes five sub-function modules: process document management, NC equipment management, NC code transmission, drawing model transmission, and process card transmission, so as to establish serial port and network port communication with the communication machine such as CNC machine tools, equipment and touch screens in the workshop. The device sends and receives process documents such as NC codes, process cards, 2D drawings, 3D models, and the management of these documents.
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3) Workshop monitoring module
The module is mainly for the monitoring of each workshop, real-time extraction of the detailed manufacturing process data such as the running status, operating parameters, current machining parts, production task completion of each workshop, and real-time adjustment equipment operating parameters, monitoring equipment health status, and according to this, it carries out on-demand distribution of production tasks, fault diagnosis, maintenance alarms and other actions. 4) Warehouse management module
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The module mainly includes four sub-function modules: RFID device management, storage management, outbound management, and inventory management, to realize RFID reader parameter and mode setting, electronic label initialization, product storage coding and information entry. 5) Product tracking module The module mainly includes two sub-function modules: product real-time status tracking and
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product real-time status query. The tracking and query information includes: basic product information, current location information of the product, current process information of the product, current processing equipment information of the product, current processing progress of the product,
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and current responsible information of the product. 6) Statistical analysis module
The module mainly includes: equipment utilization statistics, equipment failure rate statistics, product qualification rate statistics, product failure reason statistics, and workshop production statistics. It generates statistical analysis charts or reports to provide a reliable data basis for and equipment maintenance. 3.3 System architecture model
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management analysis departments to make reasonable planning decisions, rewards and punishments,
The client/server mode is the mainstream choice of the current computing mode. The client
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passes the data to the server. The server analyzes the data passed and performs related operations on the database according to the analysis result, and then returns the result to the client. The C/S mode has the characteristics of strong interactivity, safe data storage mode, short response time, low communication volume, and good processing of large amounts of data. Due to the large amount of data interaction in this system, the data needs multi-thread real-time acquisition, and requires high real-time interactive responsiveness and data security. Therefore, the classic C/S architecture system is adopted here, and the computer system and workshop are connected through the workshop Internet of Things. CNC equipment such as communication terminals, RFID readers, sensors and other hardware devices are connected, and the corresponding client is installed on each terminal client of the workshop, and real-time data information is collected and stored in the remote database server for the whole system call.
These factors are taken into account: (1) The harsh environment of the workshop and the
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complexity of the lines will have a certain impact on network cabling and data signal transmission. Therefore, the wireless connection is used for the whole workshop network, and the AP client access point client mode is selected. Under the premise of ensuring signal strength, the wireless AP is used to extend the coverage of the network to the entire workshop to realize wireless transmission of the network; (2) Considering the security of the network and data, all the workshops of the Internet of Things are integrated into the workshop Ethernet, and then connected to the corporate Internet through the firewall; (3) Each terminal device is coded by IP address in the workshop LAN, and each
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terminal device is bound with a fixed IP address to achieve accurate command and data transmission. The system architecture model is shown in Figure 3. In the workshop, the Internet of Things first needs to connect the communication terminals such as CNC machine tools and touch screens in the workshop to realize real-time communication between the upper design management department and the workshop, transmission of NC code to CNC machine tools, transmission of process documents,
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and release of tasks. Secondly, the sensors and RFID devices arranged in the workshop need to be networked, and the real-time data, positioning information and real-time data of the workshop production are converted into wireless network collection data acquisition host through the sensors
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and RFID readers arranged in the workshop, and then through the workshop. Ethernet uploads and saves data to the real-time database server for office area retrieval and visual real-time monitoring. Then, it is the networking of the execution terminal of the workshop. The workshop control center makes management and scheduling decisions according to the real-time production data of the workshop, and sends control commands to the integrated controller through the wireless network to control the intelligent execution units of the workshop. Finally, all the networks are integrated into the
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and data security.
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workshop Ethernet, and the enterprise Internet is connected through the firewall to ensure network
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Figure 3. System architecture model 4. Workshop internet of things and production line data monitoring system function research 4.1 Networking design of CNC machine tools and touch screens in the workshop
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In order to realize the real-time transmission of manufacturing data to the workshop, the NC code is transmitted to the CNC machine tool, and the process card, 2D drawing, 3D model, etc. are transmitted to the station touch screen. The working status of the CNC machine tool and the
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production data are uploaded to the office area in real time. However, the brands and models of CNC machine tools used by various enterprises are different. Due to the updating and introduction of machine tools, there are various types of CNC machine tools in different workshops, which leads to inconsistencies in the reserved network interfaces. At present, the reserved networking interface of CNC machine tools in China is generally RS232, RS485 serial interface and RJ45 Ethernet port. Therefore, for the above serial interface and network port, the networking scheme of workshop CNC
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machine tool and station touch screen is designed as shown in Figure 4.
Figure 4. Intelligent workshop CNC machine tool and workstation touch screen networking solution Due to the complex environment in the workshop, the numerous lines, and the distance from the office area, the wire line should be minimized, the line anti-interference ability should be enhanced, and the serial transmission distance should be overcome. Here, the RS232 and RS485 serial ports are converted into RJ45 Ethernet ports through the serial port server, and then connected to the wireless
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access device to be converted into a wireless network. The wireless connector is powered by the Passive Po E network cable, so that the AP can get rid of the limitation of the power access point, so that the AP The wireless access device is directly pasted on the CNC machine tool, and then wirelessly connected with other APs through the AP client access point client mode of the wireless access device, and all the machine tools and touch screens in the workshop are set to a fixed IP
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address and a MAK address. In the local area network between the established CNC machine tool and the server, the communication and file interaction are realized by accessing the shared files under the respective IP
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addresses. The network module of the machine tool is similar to the working mode of the PC network card, and consists of hardware and software. Hardware refers to the network card and network card driver, and the software is the network communication module of the machine tool. 4.2 Design and function realization of intelligent workshop wireless sensor network 1) Design of workshop wireless sensor network
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In view of the complex environment and high interference of the workshop, the wireless sensor network of the workshop needs to arrange the sensor nodes reasonably, and collect the real-time working state of the equipment through the sensor to collect the signal light of the equipment, the magnetic flux of the motor or directly collect by the PLC, through different sensors.
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The sensor nodes arranged in the workshop continuously transmit the digital and analog signals to the coordinator. The coordinator transmits the signals to the processor for AD conversion and signal amplification through the serial port asynchronous communication, and then converts the signals into wireless signals through the wireless network card. The wireless monitoring video signal transmitted by the high-definition camera is sent to the wireless sensing gateway together, and finally enters the workshop local area network and is stored in the database server, and the application terminal such as the control center converts the collected voltage, current and other signals into corresponding collection parameter values through a calculation program. The design of the workshop wireless
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sensor network reference layout scheme is shown in Figure 5.
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Figure 5. Workshop wireless sensor network design architecture 2) Design of workshop status monitoring function The wireless sensor network communicates with many sensor nodes arranged in the monitoring area and self-organizes through the aggregation node, and finally transmits the collected data
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information to the remote task management node for related processing. The sensor node usually consists of the following four parts:
The sensor module is used to collect various information of the object to be collected in the
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monitoring area and convert the collected data into a format convenient for transmission. The processor module is used to store and process the data collected by the sensor module. The wireless communication module is configured to convert the processed data signal into a wireless signal, and exchange control information with other sensor nodes, and send and receive data. The energy supply module is used to energize the sensor nodes to enable them to function properly.
After the above sensor nodes are powered on, the parameters are initialized, the input signal is
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converted by AD, the corresponding channel is selected, and the data information collected by the sensor is sent to the coordinator according to the set frequency, and then transmitted to the processor for processing according to the asynchronous serial communication protocol. After that, it is converted into a wireless signal through a wireless network card, and is connected to the workshop
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LAN and the Wi Fi/GPRS network, and finally the collected manufacturing data is displayed in the smart phone terminal and the control center.
4.3 Design and function realization of the network in the workshop radio frequency 1) Design of workshop radio frequency identification network The intelligent workshop radio frequency identification network is established, so that all entities in the workshop have their own unique identification, and their attributes, processes and locations can be queried at any time. This provides great convenience to the management. On the one hand, it can track and query the processing progress of WIP at any time. The time required for each process is stagnation, and the cause is found in time and intelligently dispatched. On the other hand, it can be tracked, avoiding the problems of manual registration in traditional workshops, difficulty in finding tools and tooling. The smart factory radio frequency identification network reference architecture is
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shown in Figure 6.
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Figure 6. Smart factory manufacturing workshop radio frequency identification network design architecture
The architecture generally follows the IoT general architecture system of the sensing layer, the network layer, and the application layer. The bottom-up is an RFID tag that corresponds to the entity object, and the static data is stored, and the RFID reader perceives the entity object. 2) Intelligent production workshop production line data monitoring function design The communication process of the RFID application system is that the reader/writer module is connected with the host computer via the USB/RJ45/RS232/WIFI interface, and receives and executes the commands sent by the host computer one by one, and finally returns the executed result information to the upper computer.
When the host computer sends the command data block to the RFID reader module, the interval between adjacent characters must be less than 12ms. Otherwise, the previously sent data will be
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directly discarded by the RFID reader module, and the data after 12ms will be treated as a new one. The command data block is re-received until the read-write module receives the correct command, executes the command, and returns a response to the reader module. The process of sending the response data by the reader/writer is actually the process of returning the execution result and the response data to the upper computer after the reader receives and executes the command sent by the upper computer, which is a complete communication process. The memory of the electronic tag can be logically divided into four memory areas, each of which typically contains
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one or more memory words. The four storage areas are an EPC area, a TID area, a user area, and a reserved area, respectively. Each product is attached to the warehouse with an electronic label, and the product code and other information is written into the electronic label storage area, and the relevant information is entered into the database. The product code finds the unique product information of the database. By
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arranging the corresponding readers and configuring the client program at the appropriate position in the workshop, the principle of “one read, one end and multiple bits” is followed. When the product passes through the reader, it will be automatically recognized, and the current location, current state,
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time and other information will be written into the electronic tag storage area according to the fixed encoding format or the database data will be updated in real time according to the number, as shown in Figure 7, thus realizing the real-time tracking query for the workshop product.
Lathe area
Data input Electronic label 2
Reader 2 (lathe 2)
Electronic label 4
Write and update the database
Return data
Query command
Reader 3 (lathe 3)
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Database server
Client
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Read tag encoding Electronic label 3
Reader 1 (lathe 1)
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Electronic label 1
Milling area
Figure 7. Using RFID technology to implement product monitoring methods 5. Production line real-time data monitoring system results analysis 5.1 Real-time analysis
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This section analyzes and studies the operation results of product-based data acquisition and monitoring systems from the aspects of hardware and software execution efficiency and production efficiency before and after system implementation. On the hardware, Line Server runs on high-performance servers, and its processing speed is higher than that of ordinary computers or industrial computers. In terms of data and real-time signal processing, its speed can meet the requirements of industrial sites. In software design, Line Server uses a linked list to manage the connection with each device, and scans the common monitoring bits
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of each station every 480 milliseconds. In the database query speed, the database is installed locally. In terms of network, since the PLC and the Line Server are directly connected through the Ethernet and in the same LAN segment, the speed of the network transmission is related to the performance of the switch, and the transmission delay through the system tester is less than 90 microseconds. By counting the running time markers of the PLC, the machining cycle of each station includes
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an online machining cycle and an offline machining cycle. The online machining cycle process includes: the workpiece reaches the fixture position, the feedback of the confirmation signal, the device startup processing, the upload of the processing data, and the device reset. There are no data
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acquisition and control signals for offline processing.
The time consuming of the data acquisition system and the control system is the difference between the time taken by the line processing cycle and the time taken by the offline processing cycle. Table 1 records the online and offline processing time statistics for a certain month. The timeconsuming fluctuations of different stations are relatively large, which is related to the process complexity of different stations. Here, the relative size of the system time-consuming and offline
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processing time is mainly analyzed. Figure 8 shows the online machining cycle, offline machining cycle, and average time consumption. The longer the line passes through the x-axis, the shorter the time it takes for the data acquisition and control system.
Functional average time consuming Offline processing cycle Online processing cycle
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Time
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15 10 5 0
1
2
3
4
5
6
7
8
9 10 11
Station
3 1 2 Cycle and time consumption
Figure 8. Online and offline processing cycle diagram of each station in a month Table 1. Average processing time of online and offline for each station in each month Station (OP
Offline
Functional
Production
Production
processing
processing
average time
line MES
line MES
cycle (s)
cycle (s)
consuming (s)
function
function
average time
overall time
consuming (s)
consuming (s)
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No)
Online
12
14.10
13.10
0.80
-
-
21
12.50
11.40
0.70
-
-
32
9.40
8.50
0.50
-
-
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8.20
7.20
0.80
-
-
54
10.10
9.10
0.70
-
-
61
16.90
16.30
0.80
-
-
72
6.80
6.20
0.90
0.42
5.00
83
0.90
0.80
0
-
-
94
0
0
0
-
-
102
19.70
18.80
0.60
113
0.08
0.10
0
121
14.70
13.70
0.10
134
20.20
20.10
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40
-
-
-
-
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-
0.10
-
-
After statistical analysis, the average time-consuming data acquisition and monitoring system of the single line of the production line is 0.42 seconds, which can meet the real-time requirements of the
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automation site. The entire production line completes one workpiece. The average time taken after implementing the system is about 127.6 seconds. The execution time of data acquisition and control is 5.4 seconds, which accounts for 4.10% of the processing time of the entire production line. It has basically no effect on the completion of the production plan.
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5.2 Quality control analysis
Key indicators of production process control of enterprises KPIs include rework rate, defective rate, yield rate, scrap rate and other indicators, which is an indicator to evaluate the comprehensive production efficiency of production lines. The equipment will verify its state before producing a single workpiece, and will remind the workpiece that is not qualified or produced according to the process requirements, and stop production, thus eliminating the non-conforming product and the workpiece that does not follow the process flow into the next process. The rework data table counts the number of rework per station.
The production process of this production line is complicated, the work station is more, the process is crossed, and the rework operations of different stations are different. For example, the OP90 is pre-installed for the bracket, there is no rework operation, and rework is not possible. The pre-installation failure is directly scrapped, and the OP70 is reworked. By configuring in the
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configuration interface, the workpiece will automatically reach the rework station after entering the production line again. Other stations will skip or prohibit production, the unconfigured workpiece can only be reworked in the current process. The number of times of rework can also be limited according to the relevant configuration to ensure the orderly production and process controllability. Table 2 counts the number of unqualified stations in a certain month. The total output of the production line in the month is 31217, of which 30752 is qualified, the number of workpiece rework
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is 177, and the number of processing scraps is 916. In the process of scrapping, the terminal inspection was 142 times, accounting for 14.15% of the total scrap rate. The scrap rate for the month was 2.79%. When the data collection and control system was not deployed, the average scrap rate was 3.22%, the end-of-line scrap rate was 74.39%, and the scrap rate was reduced by about 18%. The ratio
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of scrapped at the terminal was reduced. 76%. Table 2. Statistics of rework/unqualified times of each station in a month Unqualified (times)
12
486
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Station (OP No) 21
0
32
8
40
12
54
2
72 83 94 102
6. Conclusion
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113
0
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61
5
64 64 152 6
121
42
134
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This paper analyzes the related concepts of the existing smart factory and proposes the reference architecture and construction path of the smart factory. The industrial IoT system is the foundation of smart factories, and the intelligent manufacturing workshops make the core components of smart factories. Through the analysis of the related technologies of IoT and its implementation methods in the workshop, the core technologies of two IoT technologies, such as radio frequency identification and wireless sensor networks, are applied to design the industrial Internet of the workshop. The realtime tracking of the manufacturing workshop products and the real-time monitoring of the workshop
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status and the workshop equipment status are realized, which proves the correctness and feasibility of the research theory and key technologies. However, real-time monitoring of the manufacturing process is only the first step toward intelligent manufacturing. To achieve intelligent production, production factors need to have the ability of self-identification, intelligent perception, independent decision-making and knowledge learning. This requires further exploration in the areas of manufacturing IoT technology, artificial intelligence algorithms, and machine learning. In the future,
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the system can be combined with cloud service technology to hang each functional module in Yunping. The platform is engaged in leasing services, and each company rents its own modules on the cloud platform according to their respective needs and applies them to actual enterprises.
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Acknowledgement
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This work is supported by the Shaanxi Military and Civilian Integration Research Fund Project, Research of PID Controller Applied to Pubai Xigu Coal Mine Gas Drainage System(18JMR40); Project of Weinan Normal University, Research on Tuning Methods of PID Controller Parameters used in Process Control System(7YKS06).
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Wei Chen was born in Henan, China, in 1984. She received the BS degree from China University of Mining & Technology, Xuzhou, China, in 2006, and the ME degree from China University of Mining & Technology, Beijing, China in 2010. After graduation she worked as a teacher in School of Physics and Electrical Engineering at Weinan Normal University since 2010. She has got a further study in Shaanxi Normal University in 2017. She has published 12 papers and holds 3 patents. Her research interests include wireless sensor networks, and advanced control theory. Email: [email protected]
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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Wei Chen
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Conceptualization, Methodology, Writing- Original draft preparation.