A Novel Concept of CNC Machining Center Automatic Feeder

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28th International Conference on Flexible Automation and Intelligent Manufacturing 28th International ConferenceJune on Flexible Automation and OH, Intelligent (FAIM2018), 11-14, 2018, Columbus, USA Manufacturing (FAIM2018), June 11-14, 2018, Columbus, OH, USA

A Novel Concept of CNC Machining Center Automatic Feeder

A Novel Concept CNC Machining Center Automatic Manufacturing Engineeringof Society International Conference 2017, MESIC 2017,Feeder 28-30 June 1 1(Pontevedra), Spain2 2017, Vigo Manuel Barbosa , F. J. G. Silva , Carina Pimentel , Ronny M. Gouveia1 1

Manuel Barbosa1, F. J. G. Silva1, Carina Pimentel2, Ronny M. Gouveia1

ISEP – School of Engineering, Polytechnic of Porto – Department of Mechanical Engineering, Porto 4200-072,PORTUGAL

Costing models forUniversity capacity inEngineering, Industry 4.0: Trade-off DEGEIT, of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, PORTUGAL ISEP –GOVCOOP, School of Engineering, Polytechnic of Portooptimization – Department of Mechanical Porto 4200-072,PORTUGAL GOVCOOP, DEGEIT, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, PORTUGAL between used capacity and operational efficiency 1

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Abstract Abstract A. Santanaa, P. Afonsoa,*, A. Zaninb, R. Wernkeb Machining operations are extremely relevant in the current metalworking scenario. Many efforts have been made by researchers a in order to operations optimize machining trajectories and automatic machine systems. However, only been the large with Machining are extremely relevant in the scenario. Many efforts have madecompanies by researchers University ofcurrent Minho,metalworking 4800-058 feeding Guimarães, Portugal b and automatic huge productions have capacity enough to invest in sophisticated feeding systems. project intends present a solution to in order to optimize machining trajectories machine feeding systems. However, only thetolarge companies with Unochapecó, 89809-000 Chapecó, SC, BrazilThis extendproductions the autonomy machining centers using 6-axis robotfeeding to replace the operator on work piecetofeeding huge haveofcapacity enough to by invest in asophisticated systems. This project intends presentoperation. a solutionThe to system the consists in a of robot embedded in the same astructure as atosmall warehouse positioned onefeeding side ofoperation. the machine, extend autonomy machining centers by using 6-axis robot replace the operator on workonpiece The maintaining the possibility operating in in the a standard manner. With the modular construction of the to system consists in a robotofembedded same structure as a small warehouse positioned on warehouse one side ofit is thepossible machine, Abstract accommodatethe numerous work piece sizesinand engraving a QuickWith Response code atconstruction the work piece holder allows its maintaining possibility of operating a standard manner. the modular of the warehouse it identification is possible to and therefore brings flexibility to thesizes system. is expected that a Response milling machine system present an extended accommodate numerous work piece andItengraving a Quick code atequipped the work with piecethis holder allows its identification Under the concept of "Industry 4.0", production be more pushed to with be increasingly interconnected, working capability human possible towill extract useful process in line and therefore bringswithout flexibility to theintervention, system. It is becoming expectedprocesses that a milling machine equipped this control system information, present an extended with the Industry 4.0without needs. information based on a real timeintervention, basis and, necessarily, much tomore efficient. In this context, capacity optimization working capability human becoming possible extract more useful process control information, in line with the Industry needs. aim of capacity maximization, contributing also for organization’s profitability and value. goes beyond the4.0 traditional © 2018 The Authors. Publishedand by Elsevier B.V. improvement approaches suggest capacity optimization instead of Indeed, lean management continuous © 2018 2018 The Authors. Published by Elsevier B.V. This is an open access articleof under the CCoptimization BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © The Authors. Published by Elsevier B.V. maximization. The study capacity and costing models is an important research topic that deserves This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 28th Flexiblepaper Automation andand Intelligent Manufacturing This is an open access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) contributions fromresponsibility both the practical theoretical perspectives. presents mathematical Peer-review under of the and scientific committee of the 28thThis Flexible Automation anddiscusses Intelligenta Manufacturing (FAIM2018) Conference. Peer-review under responsibility of the scientific committee of the 28th Flexible Automation and Intelligent Manufacturing (FAIM2018) Conference. model for capacity management based on different costing models (ABC and TDABC). A generic model has been (FAIM2018) Conference.

developed and it was to analyze idle capacity and to design towards Keywords: Adhesive joint;used Structural adhesive; Finite Elements; Cohesive zonestrategies model; Scarf joint. the maximization of organization’s value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity Keywords: Adhesive joint; Structural adhesive; Finite Elements; Cohesive zone model; Scarf joint. optimization might hide operational inefficiency. © The Authors. Published by Elsevier B.V. 1. 2017 Introduction Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 1. Introduction 2017. Machining is a subtractive manufacturing process extensively utilized in many types of industry, from the Machining industry is a subtractive manufacturing process extensively utilizedvery in complex many types of with industry, the metalworking to prosthesis manufacturing, allowing for obtaining shapes high from accuracy Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency metalworking industryroughness to prosthesis manufacturing, formachine-tools, obtaining very the complex shapes high accuracy and very low surface [1,2]. Starting withallowing traditional evolution of with the electronics and and verysystems low surface roughness [1,2].kind Starting with traditional the evolution and control rapidly came to this of equipment throughmachine-tools, the Numeric Control [3]. In of thethe lastelectronics four decades, control systems haven’t rapidly came to increasing this kind oftheir equipment In theand lastsimultaneously four decades, CNC machines stopped featuresthrough in termsthe of Numeric a numberControl of axes[3]. directly 1. Introduction CNC machines haven’t stopped increasing their features in terms of a number of axes directly and simultaneously 2351-9789 © 2018 Thecapacity Authors. Published by Elsevier information B.V. The cost of idle is a fundamental for companies and their management of extreme importance This is an open access under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2351-9789 © 2018 Thearticle Authors. Published by Elsevier B.V. in modern production systems. In general, it is defined as unused capacity or production potential and can be measured Peer-review under responsibility of the scientific committee of the 28th Flexible Automation and Intelligent Manufacturing (FAIM2018) This is an open access article under CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) in several ways: tons of production, available hours of manufacturing, The management of (FAIM2018) the idle capacity Conference. under responsibility of the scientific committee of the 28th Flexible Automationetc. Peer-review and Intelligent Manufacturing * Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 Conference. E-mail address: [email protected] 2351-9789 Published by Elsevier B.V. B.V. 2351-9789 ©©2017 2018The TheAuthors. Authors. Published by Elsevier Peer-review underaccess responsibility of the scientific committee oflicense the Manufacturing Engineering Society International Conference 2017. This is an open article under the CC BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the 28th Flexible Automation and Intelligent Manufacturing (FAIM2018) Conference. 10.1016/j.promfg.2018.10.111

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controlled, advanced gripping systems, multiple working tables, higher machining speeds, improved accuracy, among other [4]. Software has also given a strong impulse to machining processes. 3D software allowed for parts modelling, bringing a solid help regarding the automation of the transferring process of a 3D model to a program able to produce a part. Effectively, the 3D CAD (Computer Aided Design) evolution rapidly gave rise to CAM (Computer Aided Manufacturing) software, with an improved compatibility among different software houses, making easier the the conversion of 3D models into machine language commands capable of controlling the different servomotors which are responsible for producing the necessary axis displacements [5]. Moreover, the market has required more and more flexibility from the equipment, which has been perfectly achieved by the machine manufacturers [6]. However, the demand for novelty does not stop increasing. Quality and productivity are customers’ requirements never completely satisfied by the machine suppliers. The interaction between equipment and the capacity to work without human labor for a long time has been another research focus, establishing the use of pallets into the machines and developing mathematical models to adequately manage the workflow. Moreover, the cooperation between machining companies is a current need, bringing new challenges to the machine producers in order to digitalize manufacturing systems [7]. This paper intends to conceive a machining cell able to be included in an Industry 4.0 behavior and is divided into five sections: section 1 consists of the introduction; section 2 presents a review of the literature dealing with the machining processes progress; section 3 describes the methodology used to carry out this work; section 4 contains the development and results obtained, and section 5 consists of the conclusion of the study. 2. Literature Review Effectively, the researchers’ focus presents a multifaceted shape. Fujii et al. [8] developed the design of an autonomous system allowing for determining the optimized number of pallets and corresponding handling rules, as well as the number of Automated Guided Vehicles (AGV) needed in a machining cell in order to increase its agility. Regarding the management of the machining cells considering a set of machines and parts, Won and Currie [9] developed a p-median model adopting a new similarity coefficient based on production factors that critically impact the machine cell/part family couple, taking as basis of their work the pre-existing p-median models based on the classic binary part-machine incidence matrix, showing its effectiveness through a computational comparison, in terms of computational time and results quality, when compared with other mathematical models previously developed. Moreover, the production of parts involving more than one machine (sequential machining) has been also studied. Chavoshi et al. [10] defends that performing sequential micro-machining on a single machine tool represents a considerable advantage because it is able to avoid repositioning errors, enabling much higher levels of accuracy and the use of tighter tolerances, lower refusal of machined parts, and lesser cycle time; impossible to achieve using the concept of hybrid machining. These authors [10] clearly state that “…the necessity of developing reconfigurable, precise and flexible manufacturing is a key driver of this trend”. Ozpeynirci et al. [11] also developed an algorithm able to deal with parallel machine scheduling, including tool loading, which resulted in near-optimal solutions within reasonable times. Optimization models regarding the space travelled by parts between consecutive operations have been also studied by Adenso-Díaz et al. [12], regarding the space occupied by machines, as well as separation constraints, resulting in a simultaneous machine grouping and route assignment, which lead to the minimization of the inter-cell traffic. Communication between machines is also a concern, having various protocols. Some investigations have been done in this field, being the review of Verma et al. [13] an interesting comparison among different systems, categorizing them through three major data traffic groups, namely: packet length, transmission mode, and priority of the data transfer. Moreover, the work presented by Chen and Lien [14] refers to the same issue, discussing the models able to link the machines among them via cloud technologies and services. Machines scheduling and robotics have been usually linked as a solution for complex cases. Abdulkader et al. [15] developed a generic algorithm able to find the sequence of the parts that minimizes the robottravel cycle time for each robot cycle regarding a four-machine blocking robotic cell producing identical and different part types. Other authors have been investigating autonomous machining systems and the strategies’ optimization to predict and expand the performance of milling operations [16].

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Nowadays, robotic support for machining operations is usual. Furthermore, Iglesias et al. [17] advocate that robotic machining represent a cost-saving and flexible alternative compared to conventional CNC machines which present restricted working area and limitations in the shapes that those are able to produce. However, there are some concerns due to the configuration and position of the robot facing each machining task [5]. Mousavi et al. [18] have been studying the best condition to minimize the chatter formation depending on the robot position in milling operations, improving the stability of the machining system and process. Using the typical redundancy presented by the robot, it was possible to increase the productivity in 100% without any change of the cutting parameters. Moreover, studies have been carried out regarding the need to produce small-batches in industrial machining applications using robots and including techniques such as elastic-errors-model-based compensation, sensing techniques, calibration and sensor-processing techniques and advanced-robot position and compliance control [19]. Indeed, the lack of accuracy usually presented by robots in machining operations has been also dealt by Lin et al. [20], leading to consider performing machining in the regions where the kinematic, static and even dynamic performances of the robot workspace are highest, thus tumbling machining errors and exploring the real advantages presented by the robots in this field. Some authors have introduced sensors into machine tools regarding different purposes, namely leading to measure the power consumption and relate it with the effective work produced by the machine, establishing KPI (Key Performance Indicators) in order to evaluate the sustainability of the machining process [21]. Machine sensors can even be applied to older machines not previously prepared for that, using a spindle motor current acquired by a current sensor, with good results [22]. Furthermore, the data collected also allows for the calculation of the productivity. Other authors have recently implemented sensing interfaces in order to get information about the machine-tool behavior, detecting the need of preventive maintenance and communicating with the machine manufacturer, informing about possible functioning problems [23]. Some frameworks have been developed to control machining processes based on cloud services allowing for monitoring machining processes in terms of productivity, tool wear checking, among others. The multi-sensors send their signals in real time to a server able to decide what the machines must do, controlling them [24]. This corresponds to the current requirements of the smart/advanced manufacturing companies, which need to be connected and working together in projects that are inter-linked, facilitating the communication and interaction, and reducing significantly the lead time in the execution of complex systems. Subsequently, learning models have been developed in order to manage big data provided from sensors incorporated in the machine tool, in order to improve the decisions that need to be taken in real-time, regarding the inputs received [25]. The cloud systems have also been explored in order to promote a better scheduling among a large set of machine tools, using several algorithms, including the task migration approach, which showed good results, avoiding overloaded machines and reducing the production time for each part [26]. Regarding the machining processes loading operation, Gultekin et al. [27] developed a dual gripper system for two identical machines placed linearly, using a robot to the loading and unloading operations, promoting as well the transport of the parts between them. This work intends to develop the concept of a robotic system able to load and unload a machining center, provided with parts control storage, able to manage different parts to be produced and providing information about the status of the work done and scheduled to be done, through the holders’ occupancy in the storage system. The information can be transmitted to the machining manager, allowing to centralize the information and manage a larger number of machines with fewer labor costs. 3. Methodology In order to achieve the proposed goal, several aspects were thought up in order to completely fulfil the usual needs in this field. Furthermore, a list of requirements was also elaborated, contemplating items such as: average volume occupied by each part allowing the access of the robot arm without collisions, blocks and parts warehouse system, number of blocks and parts that can be stored before and after the machining process, robot work range, controllable machine gripping system flexible enough to allow for different geometries fixture, sensors on the warehouse slot system able to provide information to the software about the existence of blocks to machine or parts already machined, block identifications allowing for the automatic selection of the machine program and information transmission system. The flowchart of the interconnection between these systems can be seen in Figure 1. Moreover, a conceptual model of communication among different cell devices was developed as shown in Figure 2.



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Figure 1 - Flowchart regarding the first approach developed

Warehouse

Warehouse

Figure 2 – Communication conceptual model for the first approach developed

In addition to the concept translated by the flowchart, a list of important topics to be accomplished was also elaborated, as follows: • The equipment must be considered as an add-on to a machining center (types formerly described); • The equipment must allow the replacement of finished products by new stocks while still in operation; • The robot must be provided with QR Code reader in order to identify the block (raw material previously cut) and provide the correct information to the CNC controller, selecting the corresponding NC program; • Contemplate a versatile holding system to be used in as many applications as possible;

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• The gripping systems assembled in the machine table must have hydraulic actuation and, depending on the parts specifications and dimensions of the machine table, they must allow for the assembly of more than one hydraulic vice adequate to the family of parts to be machined in each cell. • In the future, the equipment must allow remote control and automatic displacement of the warehouse system, being moved by AGV (Automatic Guided Vehicle). After establishing the concept, as described by the flowchart, the design stage was started, giving rise to the peripheral equipment able to automatize the load and unload operations, completely integrated with the CNC machine program. 4. Results and Discussion It is intended that this warehouse assumes a modular fashion and is able to be connected to CNC lathes, CNC Milling machines, CNC Grinding machines or CNC Electrical Discharging Machines (EDM). Moreover, it is expected that a milling machine equipped with this system presents an extended working capability without human intervention, becoming possible to extract more useful process control information, in line with the Industry 4.0 needs. Furthermore, the production monitoring and absence of workers permanently allocated to the machines is another important goal. Based on these principles and the different requirements previously defined, the development was carried out taking into consideration some details, as follows: • • •

• •

• • •

• •

•

The connection between the machine and the peripheral equipment needs to be as solid as possible; The blocks warehouse must be removable, allowing for future drive system through an AGV; The connection of this warehouse must be accurate and adjusted by conical pins in order to be coherent in terms of accuracy with the robot positioning. Alternatively, the robot must read the warehouse in three points (after secure connection), defining the plane where the parts are located in the warehouse. In this case, the robot must receive information about the end of the warehouse connecting process, only starting the plane identification process after complete stabilization of the warehouse; The same situation is valid for adjustment of the warehouse by conical pins; Each block needs to be provided of a QR code in order to send this information to the machine NC through the reading operation performed by the reader in the robot arm tip. This information is sent to the controller, able to select the program needed for this part, as well as the corresponding tools. This operation must be done with care in order to avoid the presence of lubricants in the QR Code area, preventing difficulties in the reading process; If the QR code does not correspond to any of the programs available in the CNC machine, the block must be automatically segregated and changed for another, sending a signal to the process monitoring central, identifying the slot where the block was deposited; Depending on the block and desired part, the machine table will be automatically adjusted in order to combine the movements of the robot with the one promoted by the table, matching the local where the block will find the gripper; After the corresponding approximation motion promoted by the robot, a signal will be sent to the gripping system in order to close it, tightening the block in the machining position. The block or workpiece will be held by different holding systems, depending on the machining process used. As can be seen in Table 1; Then, robot exits the work area, sending a signal to the NC in order to start the machining process; After the machining process has been completed, the robot engraves a new QR Code in a previously determined area (the first one was removed by the machining process or can be deteriorated by the gripping system, if located in that area). This QR Code will be needed for further operations and/or storage for final assembling; Then, robot takes the part from the machine and, through information previously received from the central monitoring system (CMS), the part will be stored in the proper slot corresponding to the parts already machined;

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Subsequently, the slot sensors will send a signal to the CMS which, by its turn, sends information to the robot regarding the location of the next block to be machined, starting again the cycle; When each cycle is finished, the system analyses the number of blocks currently stored in the warehouse, defining if the quantity of blocks is low enough to send a message to the CMS, asking for the warehouse restocking or, if using AGVs, giving order to the AGV to move from the reference area to the cell area, in order to replace it, during a machining task. In this case, the robot needs to assume a safety posture, in order to avoid collision during the warehouse changing process;

Table 1- Holding systems for blocks/workpieces, regarding the different types of machining processes considered

Application

Workpiece holding system

Block fixture

Final product

Custom machining tools

Electro-erosion tools

General purpose machining

• • • • •

If no AGVs are used, the warehouse is previously divided into two safety zones so it allows the removal of product and placement of new workpieces in one side while the milling machine is still being fed by the other; Moreover, the system should be thought to support an automated quality control system using artificial vision or 3D scanning, depending on the precision required by the machined parts; The cell must be as modular as possible, allowing for the assembly in a pre-determined range of CNC machines, depending on the access to the work area; Moreover, the cell is thought to let its application on CNC twin mirror machines, since the machining cycle of the parts is long enough to support the robot tasks; The robot needs to be selected as a function of the working range needed, as well as the common features assigned to the robots. Moreover, a collaborating robot can be selected in order to avoid safety protections and simplify the cell working area [28].

After the above referred specifications, the concept can be designed in a draft mode, allowing understanding how the layout will be. Figure 3 tries to describe the first approach undertaken regarding the cell design and its connection to (a) one or (b) two CNC milling machines. In the case (a) it was selected a HAAS VF2 Machining center to be used as showcase. In Figure 3b) it is shown a twin-mirror system with two small machines, MiniMill model, from HAAS. The warehouses can be also seen in the cells, in the first case linked just to the CNC Milling Center that intends to serve, while in the second case the warehouse promotes the linkage between the two machines allowing for a correct task schedule, feeding each machine when the other is machining. Furthermore, the cells need to be properly installed into the shop floor if AGVs will be used, allowing for a proper workflow, considering the supply chain system adopted into the company and its linkage to the central warehouse. Moreover, the layout should

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include space enough between the AGVs lines and the machines, leading for the human intervention when preparation, tools change or maintenance is needed, preventing workflow impediments. With this system, the CMS receives the various information from all devices responsible for sending signals to unlock different actions of the actuators and autonomous devices. Centralizing the information in a unique system allows for a better coordination of the actions. In each moment, it is possible to have complete reports about the number of parts produced, tools wear monitoring provided by the CNC machine controller, expected time to have completed some order and so on, giving rise to real industry 4.0 behavior.

Figure 3 – Aspect of the cell operating with just one CNC milling machine (a) or twin mirror CNC milling machines (b)

5. Conclusions The main goal of this work was to conceive a cell able to be connected to one or two machining centers, allowing for automatic feeding of the machine or machines through a robot and several sensors and automatic actuators, avoiding the necessity of human labor for the constant loading and unloading tasks. Thus, this cell can be personalized depending on the customer needs in terms of robot used, shelve size and type of sensors used, the machine may hold more than one vice, extending the diversity of parts to be processed in each machine and the parts codification can easily be dealt with through QR codification. Moreover, the cell may be integrated into a more complex system with AGVs, allowing as well the automatic change of the warehouse and corresponding shelves, when full of parts already machined. This concept can be easily applied in companies searching for high production cadencies with elevated versatility and agility, leading to a complete perception of the current production state at any time, reducing drastically the labor need around these equipment. The concept translates perfectly the Industry 4.0 concepts. Acknowledgements The Authors also thank the cooperation and financial support provided by LAETA/CETRIB/INEGI Research Center, as well as FLAD – Fundação Luso-Americana para o Desenvolvimento (Proj. 116/2018). References [1] [2] [3] [4] [5]

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