Modeling and simulation in intelligent manufacturing

Computers in Industry 112 (2019) 103123

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Computers in Industry journal homepage: www.elsevier.com/locate/compind

Modeling and simulation in intelligent manufacturing Lin Zhanga , Longfei Zhoub,* , Lei Rena , Yuanjun Lailia a b

Beihang University, Beijing 100191, China Massachusetts Institute of Technology, Cambridge, MA 02139, USA

A R T I C L E I N F O

A B S T R A C T

Article history: Received 28 April 2019 Received in revised form 16 July 2019 Accepted 10 August 2019 Available online xxx

With the continuous deepening of the application of information technology in the manufacturing field, the informatization of manufacturing systems has developed from unit digital manufacturing to integrated networked manufacturing, and then to comprehensive digital, networked and intelligent manufacturing. As a comprehensive information technology integrating computer, model theory, and scientific computing, the modeling and simulation technology plays an irreplaceable role in the development process of manufacturing informatization and is widely applied in all stages of product life cycle containing design, production, testing, maintenance, procurement and sales. This paper reviews and summarizes the research and application of modeling and simulation technology in manufacturing, and analyzes typical simulation techniques in manufacturing from aspects of manufacturing unit simulation, manufacturing integrated simulation and manufacturing intelligent simulation. © 2019 Elsevier B.V. All rights reserved.

Keywords: Intelligent manufacturing Modeling Simulation Review

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manufacturing unit M&S . . . . . . . . . . . . . . . . . . . . . . . . . . . Design-oriented M&S . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Production-oriented M&S . . . . . . . . . . . . . . . . . . . . . 2.2. 2.2.1. Manufacturing process simulation . . . . . . Machining process simulation . . . . . . . . . . 2.2.2. Simulation-based planning and scheduling 2.2.3. 2.3. Testing-oriented M&S . . . . . . . . . . . . . . . . . . . . . . . . Software simulation based virtual testing . 2.3.1. Virtual instrument . . . . . . . . . . . . . . . . . . . 2.3.2. VR-based virtual testing . . . . . . . . . . . . . . . 2.3.3. Manufacturing integrated M&S . . . . . . . . . . . . . . . . . . . . . . Virtual Prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. 3.2. Simulation-based Acquisition . . . . . . . . . . . . . . . . . . High-Level Architecture . . . . . . . . . . . . . . . . . . . . . . 3.3. Manufacturing intelligent M&S . . . . . . . . . . . . . . . . . . . . . . New-generation digital model . . . . . . . . . . . . . . . . . 4.1. Model engineering . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Intelligent cloud simulation . . . . . . . . . . . . . . . . . . . 4.3. Industrial big data-based M&S . . . . . . . . . . . . . . . . . 4.4. Edge simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Embedded simulation . . . . . . . . . . . . . . . . . . . . . . . . 4.6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

* Corresponding author. https://doi.org/10.1016/j.compind.2019.08.004 0166-3615/© 2019 Elsevier B.V. All rights reserved.

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1. Introduction After development for over sixty years, the modeling and simulation (M&S) technology has become another important means for humans to know and transform the objective world besides theory and experiment. The applied range of M&S covers almost all aspects of economy, society and military, especially for key fields concerning national strength and national security, such as aerospace, military, medical treatment, traffic, information, biology, materials, energy, manufacturing, agriculture, education, etc. A common feature of these fields is that the research objects are with extremely high complexity, uncertainty and nonlinearity. Even one system has both quantitative and qualitative, continuous and discrete characteristics simultaneously. These characteristics make it difficult to research the system comprehensively and deeply by using traditional theoretical research methods. These characteristics also make it necessary or even the only choice to apply M&S to study these complex systems. The M&S technology shows unique advantages in solving practical problems [1–5]. In the manufacturing field, the application of M&S technology can be traced back to the 1950s. After decades of development, the application of M&S has extended to almost every stages of the product life cycle, including design, production, testing, maintenance, procurement, supply, sales, and after-sales services. At present, M&S plays an extremely important role in the manufacturing field. By integrating with information technology, manufacturing systems are gradually developed to be digitized, networked, collaborative, personalized, service-oriented and intelligent. The demand for M&S technology in the manufacturing industry is also becoming bigger and bigger. In 2000, the Defense Advanced Research Projects Agency, Department of Commerce, Department of Energy and National Science Foundation jointly released the Integrated Manufacturing Technology Roadmapping Project (IMTR) which presented six major challenges that the manufacturing industry would face in

the future, including being lean and efficient, improving customer response speed, being comprehensively interconnected, maintaining environmental sustainability, knowledge management, and applying new technologies. Meanwhile, IMTR proposed four key technical fields to meet these challenges including:    

Information Systems for Manufacturing Enterprises (IS) Modeling & Simulation for Manufacturing (M&S) Manufacturing Processes & Equipment (MPE) Technologies for Enterprise Integration (TEI)

The M&S was considered as one of the key technologies to copy with these manufacturing challenges. The development process of manufacturing informatization is generalized into three basic paradigms including: digitized manufacturing, digitized networked manufacturing, and digitized networked intelligent manufacturing which is also called the New-Generation Intelligent Manufacturing [6]. Referring to this point of view, this paper generalizes the M&S technology in manufacturing into three stages:  Unit M&S for Digitized Manufacturing (Manufacturing Unit M&S)  Integrated M&S for Digitized Networked Manufacturing (Manufacturing Integrated M&S)  Intelligent M&S for Digitized Networked Intelligent Manufacturing (Manufacturing Intelligent M&S) The evolutionary process of M&S technology in manufacturing is shown in Fig. 1. This is just the general technology development of modeling and simulation in manufacturing over time, even though it differs between different countries, industries, company sizes, involved functions, etc. In this paper, the development and application of M&S technology in manufacturing is reviewed systematically, and the development trend of related technologies

Fig. 1. The evolutionary process of M&S technology in manufacturing.

L. Zhang et al. / Computers in Industry 112 (2019) 103123

is prospected. Intelligent M&S is not suddenly proposed but has suffered a development process from quantitative to qualitative. Unit M&S lays the technical foundation for intelligent M&S, and integrated M&S realizes information interconnection and task coordination in intelligent M&S. Then, based on the unit M&S and integrated M&S, it finally develops into an intelligent M&S system. 2. Manufacturing unit M&S Through applying digital technology, manufacturing companies can reduce the time and cost of product research and development and satisfy the individualized requirements of customers better. On this basis, the rationality of design and the quality of products are to be improved and the response speed to market requirement will be accelerated [7]. The M&S technology is an important aspect for manufacturing systems to realize digitalization, and not only supports engineers to comprehensively analyze the complex manufacturing systems, but also assists decision makers to study the impact of operation strategies on system performance [8]. The M&S technology has been applied in almost every stage in manufacturing including design, production, testing, maintenance, procurement and sales. Model-driven methods have also played an important role in enterprise modeling and information systems [9,10]. In this section, we mainly discuss the application of M&S in some typical manufacturing processes like design, production and testing. 2.1. Design-oriented M&S As one of the most creative activity in the product life cycle, the product design process is always with high uncertainties which may lead to potential flaws of product design schemes. Obviously, in the product life cycle, the earlier defects will cause the larger losses to manufacturers. Therefore, it is of high significance to apply M&S to avoid potential flaws and mistakes in the early product design stage. The design process of complex products often involves several different disciplines and fields. Design of different types of products also involve different fields. Taking mechanical products as an example, the product design activities generally involve mechanical structures, electronics, control, pneumatics, etc. For the same product, the design scheme of each single field needs to be refined and optimized through the M&S technology. What is more, there are generally specialized design software and tools in each field [11,12]. Structural simulation involves statics simulation, dynamics simulation, kinematics simulation, fluid mechanics simulation, thermodynamic simulation, etc., and often needs to be combined with the finite element analysis method [13]. Statics simulation is mainly applied to analyze the stress, displacement and deformation of the product under stable load to test the strength and stability of the product. Dynamics simulation is generally applied to test the product structure and state under varying loads, including modal analysis, transient analysis, spectral analysis, and vibration analysis. Kinematics simulation is used to simulate the motion trajectory, velocity, and acceleration of products. Fluid mechanics simulation is applied to analyze the fluid flow, heat transfer, velocity field, pressure field and so on. Thermodynamic simulation is generally applied to analyze the heat flow, temperature fields and heat transfer characteristics of the whole system. In the electronic circuit simulation, the actual operation of electronic circuits is simulated based on the established model. Especially for complex integrated circuits, the physical circuit design consumes high costs. As a result, the M&S method is applied for almost all integrated circuits to test and improve the functions and performance of design schemes [14]. Control field is one of the

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most mature fields of M&S application. In the control simulation, the computers and physical simulation systems are applied to simulate the operational process of controllers, actuators and system environment [15]. In this paper, we focus on modeling and simulation technology in manufacturing industry in which M&S is mainly applied in product design part. But for the success of new product design, we do also need other important technologies (e.g. marketing, standardization, project management, innovation management) in addition to modeling and simulation methods. As manufacturing systems become more networked and integrated, the M&S for product design also gets more integrated, which we will discuss in detail in Section 3. 2.2. Production-oriented M&S The production execution process is one of the most important and complex stages in the product life cycle. The quality of production execution process directly affects the final product quality and performance. In the production execution process, M&S is mainly applied in manufacturing process simulation, machining process simulation, and simulation-based planning and scheduling. 2.2.1. Manufacturing process simulation As an important activity in the product life cycle, product process design is the bridge to connect the product design activity and the production activity. In order to guarantee the feasibility and effectiveness of the designed process plan, we need to simulate the designed process plan before the production execution. Through simulation, those unreasonable factors in the process plan are reduced, and then the designed process plan is improved. In the field of Computer Numerical Control (CNC) machining, process simulation usually includes tool path simulation, motion interference detection simulation (geometric simulation), machining characteristic simulation and machining accuracy simulation (physical simulation) [16,17]. In the field of welding, process simulation refers to the simulation of the welding process through modeling and numerical analysis to evaluate the welding process and optimize the technological parameters [18]. Through integrating a simulation module into the existing basic process design system, the designed original machining process plan was simulated, and further modified and optimized according to the generated simulation results [19]. The process design-oriented M&S technology can reduce the time and cost of product process design and improve the quality of process design schemes. But the current process design-oriented M&S technology still needs further research and exploration. 2.2.2. Machining process simulation The machining process simulation refer to the simulation process for a single processing machines or several related processing machines. It is the foundation of machining process simulation to build a simulation model with kinematics and dynamics characteristics. Through running the machining process simulation model, engineers can visually observe the continuously varying physical characteristics and parameters such as tool cutting trajectory, material deformation, stress field, temperature field, vibration, etc. CNC machining simulation is an important part of machining process simulation technology, and CNC machining simulation is usually used for CNC program optimization and operator training. The CNC machining simulation technology develops relatively mature, and there have been a lot of commercial simulation tools so far. The functions of CNC machining simulation tools mainly

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include CNC program verification, tool motion simulation and entity comparison. In addition, by combining process simulation technology with Virtual Reality (VR) technology, a virtual environment consistent with the real CNC machining environment can be constructed. This virtual environment can be used to simulate the real control system and machining processes, and the activity is also called “Virtual Machining”. Before actual machining, technicians can use virtual machining technology to visually observe the machining results, and then evaluate and optimize the machining program [20]. In recent years, research on machining process simulation mainly includes: robot program optimization based on online simulation [21], optimal control of production processes based on virtual simulation [22], and simulation-based workload and job release control [23]. 2.2.3. Simulation-based planning and scheduling The production management problem is not only a matter of modeling and simulation but also affected by other factors of the whole supply chain. For the optimization problem of the whole supply chain, there are other technologies showing more efficient performance than M&S. But M&S has been definitely used in both designed optimization problems and processing optimization problems in manufacturing enterprises. Especially for the realtime scheduling optimization problem in a single enterprise environment, the dynamic data-driven simulation technology is effectively applied to improve the scheduling performance and response to interference events and shows usefulness and effectiveness. Production planning and task scheduling are also the important parts of production process management, and are also an important application of M&S in the production process. Since the 1980s, simulation-based scheduling in manufacturing systems has been focused by researchers and enterprises. In some emerging manufacturing industries, the simulation-based scheduling technology has been widely applied [24]. There are a large number of stochastic events and high uncertainties in the actual manufacturing systems. Disturbances like machine breakdown in the production execution process can always result in invalidation of original plans and schedules [25]. Hence, it is difficult to apply deterministic methods to solve the actual scheduling problem in manufacturing systems. M&S has become an effective method to solve the problem of

manufacturing dynamic scheduling. Compared with deterministic methods, the simulation-based scheduling method can generate a feasible task execution plan within a shorter time [26]. The M&S technology for manufacturing task scheduling can roughly be categorized into two categories, namely offline simulation-based scheduling and online simulation-based scheduling. Early in its development, M&S was basically the offline simulation by which enterprises simulated specific scenarios and obtained statistical results. But the evolution process of the system is not visible [27]. Recently, simulation-based optimization has been combined with lean production [28]. And the discrete event simulation method was also used to select process improvement initiatives [29,30]. Studies on online simulation-based scheduling include simulation-based dynamic planning and scheduling [31,32], dispatching rules-based simulation [33,34], simulationbased hybrid backwards scheduling [35], machine criticality-based simulation [36], etc. With the rapid increase of the computing power, the simulation running time is greatly reduced, which creates conditions for online simulation technology. In the online simulation-based scheduling, the simulation model is connected to the real manufacturing system, and the simulation model is adjusted according to the real-time data of the real system. The scheduling model is then simulated based on real-time data and simulation results are used to improve the current scheduling strategies in the real system, realizing interactive scheduling [37]. The simulation results can also be sent to the operators in workshops through terminal devices to enhance their capacity of responding to disturbances. The application of real-time simulation technology in complex manufacturing systems still faces great challenges [38]. Fig. 2 shows an example of a simulation-based scheduling model for an actual manufacturing system environment, and this model is built with Simio. In addition, other studies of simulation-based scheduling include combination of simulation with intelligent optimization algorithms [39], combination of simulation and expert systems [40], real-time scheduling based on dynamic data-driven simulation (DDDS) [41], event-triggered scheduling method [42], agentbased scheduling [43] and application of simulation in logistics [44], 3D printing [45] and supply chain management. The application of online simulation technology in complex manufacturing systems still faces great challenges.

Fig. 2. A simulation-based scheduling model for an actual manufacturing system.

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2.3. Testing-oriented M&S Testing is a very important part for product manufacturing during the product life cycle. The quality and efficiency of the testing process directly affects product quality and user experience. Main applications of M&S in product testing include: virtual testing based on software simulation, virtual testing based on virtual instrument and virtual testing based on VR. 2.3.1. Software simulation based virtual testing Software simulation based virtual testing generally refers to the testing of electronic components, especially integrated circuit chips, through software simulation method at the early stage of product development. By using test simulation software, we can virtually test the product specifications and requirements before production of samples. The purpose is to identify defects in product design as early as possible, shorten product development cycles, and reduce development costs. Virtual testing can simulate the actual product testing environment, but the complexity and testing requirements of different products vary widely [46,47]. The virtual test bed is a typical integrated test system based on software simulation [48]. It integrates multiple models developed in different languages, and provides comprehensive design and simulation capabilities for complex systems through software simulation. The virtual test bed is also applied in testability analysis and fault diagnosis. How to rapidly and accurately build the models of test software, supportive hardware, and measured objects is important for this type of virtual testing technology. 2.3.2. Virtual instrument Virtual instruments are actually the virtual measurement tools with virtual dashboards and computing power. Virtual instruments are based on the virtualization and sharing of resources (i.e., computers and measuring instruments), and integrate digital signal processing techniques. Virtual instruments combine the common functions of test instruments with other special functions, and change the definition mode of measurement instrument functions from manufacturers to users. With virtual instruments, users can combine common modules with one or more functions according to their testing requirements, and invoke different software modules to form various instrument functions. When the testing requirements change, users only need to add or replace some functional modules of the virtual instrument to build a new measurement instrument without having to repurchase and deploy the entire instrument [49,50]. Compared with traditional measurement instruments, virtual instruments have the advantages of scalability, reconfigurability, high efficiency and low cost. In addition, virtual instruments can be applied in some special measurement environments where traditional instruments are difficult to perform, such as toxic, hazardous and remote environments [51]. 2.3.3. VR-based virtual testing As a new type M&S technology, Virtual Reality (VR) provide users with immersive virtual environment and makes it possible for people to interact with real-time 3D digital models. VR-based virtual testing technology is also an important application and research direction of testing-oriented M&S. VR-based virtual testing is of high flexibility because we can simulate the changes in physical parameters in the actual manufacturing system by modifying the relevant parameters of the 3D model. The configurability of 3D virtual models can support changes in task requirements. VR-based virtual test technology has been applied in product testing process in the manufacturing field. In some special testing environments, the real testing system is simulated based on

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establishing a 3D VR model of the real test scenario. Based on VR, article [52] established a virtual vehicle test system in which signal acquisition and processing technology and automatic control technology are both applied to model and simulate the actual operating conditions of the vehicle, thereby evaluating system performance from various aspects. In addition, VR-based virtual testing technology is also applied in optical testing of products [53]. With the further development and maturity of VR technology, VR-based virtual testing technology will be more widely and deeply applied in the testing process of manufacturing industry. 3. Manufacturing integrated M&S As manufacturing systems become more and more digitized and networked, enterprises have gradually integrated different information systems such as CAD, CAE, CAPP, even ERP and MES. To support the design, operation and optimization of integrated manufacturing systems, simulation techniques have to evolve from single-system oriented unit simulation technologies to multisystem oriented integrated simulation technologies. In this section, we discuss three typical integrated simulation techniques: Virtual Prototype (VP), Simulation Based Acquisition (SBA), and High-Level Architecture (HLA). 3.1. Virtual Prototype VP is a most typical integrated simulation technology used in the manufacturing field. Based on concurrent engineering, VP integrates traditional design and simulation technologies such as CAD and CAE, as well as information technologies such as VR to support multidisciplinary collaborative design of complex products. On the basis of CAD, VP further contains key features of real products, such as functional characteristics, material properties, etc. VP is actually a collection of multi-domain digital models [54,55]. Although VP is mainly developed for product design, it also involves various stages after design, such as processing, assembly, and operation. VP can be regarded as an advanced simulationbased design method based on parallel engineering to further improve the parallelism and efficiency of product design. VP plays an increasingly important role in the development of complex products. With VP technology, companies can reduce the time and cost of product development, improve product quality, and enhance competitiveness. In the product development process, as the parallelism degree increases, errors in decision-making occur more frequently. Through transforming the parallel of upstream and downstream activities to multi-domain collaboration, VP realizes effective multi-expert collaborative design [56]. Literature [57] proposed the concept of complex product virtual prototype engineering, and analyzed VP technology from the perspective of system engineering. Literature [58] applied VP technology to design and implement a modeling and simulation system for offshore cranes. Literature [59] established a VP-based simulation platform for hardware and software co-design and testing in multiprocessor systems. 3.2. Simulation-based Acquisition Simulation-based Acquisition is a new acquisition approach proposed by Department of Defense of the United States (DoD) which attempt to facilitate reuse of M&S tools and resources across acquisition functions and program phases, and across programs within DoD. It is a concept in which M&S as a resource is more efficiently managed in the acquisition process include design, development, test, production, deployment, logistic support, and disposal. The purpose of SBA is to substantially reduce the time,

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resources, and risk associated with, the entire acquisition process, and to increase the performance of the system [60]. SBA deeply combines VP-based M&S and manufacturing technologies. At present, the application of SBA in the development of complex products is gradually deepening. In order to evaluate the effectiveness of SBA, specific approaches were proposed to measure the size of formalism-based simulation models and quantitatively estimate efforts of system modeling [61]. Four methods for atomic models and four methods for digraph models were proposed including their merits and demerits. The development cost for simulation models in SBA was also considered and cost Estimation of Hybrid System Models in SBA was studied [62]. 3.3. High-Level Architecture High-Level Architecture (HLA) is an open, object-oriented architecture which was proposed for distributed simulation [63]. In HLA, object models in different levels are built in an objectoriented way, and simulation systems and components can be reused. Run Time Infrastructure (RTI) is the real-time operating platform for implementing HLA. HLA/RTI plays an important role in the manufacturing field. The HLA-based distributed collaborative virtual assembly system can support designers in different locations to perform real-time virtual collaborative assembly in a virtual environment [64]. Literature [65] proposed an integrated collaborative design, simulation and optimization platform based on HLA, and applied this platform in design and development of trains. Literature [66] proposed a method for mapping the output variables of one model to the input variables of another model, and proposed the corresponding HLA application layer program framework. HLA is now frequently coupled with FMI/FMU, especially in Europe [67–69]. HLA is also applied to model and simulate the lunar base in which different research groups established their own modules and communicated with each other [70]. For complex manufacturing systems, especially multidisciplinary, distributed, collaborative design and manufacturing systems,

how to improve the efficiency of system integration through HLA/RTI still needs further research and practice. 4. Manufacturing intelligent M&S Deep application of new-generation IT and AI techniques in manufacturing has facilitated the generation and development of the new-generation intelligent manufacturing. The new-generation intelligent manufacturing emphasizes the deep integration and efficient collaboration between people, information systems and physical systems. It mainly consists of three functional systems (intelligent products, intelligent production and intelligent services) and two support systems (intelligent industrial network and intelligent manufacturing cloud) [6]. M&S technology will play a more important role for this new manufacturing system to achieve collaborative optimization between people, information systems, and physical systems [71]. Fig. 3 shows the model of future intelligent manufacturing enterprises, based on the upper cloud platform and the underlying embedded system. This is a flexible and intelligent manufacturing system. It is necessary to develop new types of simulation technologies and tools to support future intelligent manufacturing enterprises under this system architecture. New features of intelligent manufacturing systems lead to new demands for M&S of supporting deep integration between people, information systems, and physical systems. The new M&S technology for new-generation intelligent manufacturing needs to be proposed as we called “manufacturing intelligent M&S”. In this section, we discuss typical manufacturing intelligent M&S technologies including: new-generation digital model, model engineering, intelligent cloud simulation, industrial big databased M&S, edge simulation and embedded simulation. 4.1. New-generation digital model As one of the key support technologies for new-generation intelligent manufacturing, New-Generation Digital Model (NGDM) combines typical M&S and new information technologies such as

Fig. 3. The model of future intelligent manufacturing enterprises.

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CPS, IoT, AI and VR. For specific simulation demands, highly reliable NGDM need to be built based on the structure and real-time status of the physical system. With NGDM, not only offline system analysis and prediction, but also online interaction with the physical system can be realized. As a typical NGDM technology, Digital Twin (DT) is actually an extension and development of traditional VP technology. Although DT cannot be exactly the same as a physical object, a suitable DT model can be built according to different simulation requirements to adapt to different scenarios. Siemens company proposed that enterprise DT includes product DT, production process DT and equipment DT. These three types of DTs are integrated to form a virtual enterprise based on models and automation technologies to simulate, test and optimize the real manufacturing systems before actual production [72,73]. DT is also applied in aircraft life prediction [74], aircraft structural damage monitoring [75], spacecraft real-time monitoring and maintenance [76,77] and multi-robot coordination [78]. Fig. 4 shows a digital twin of an industrial robot which was developed by a laboratory at Beihang University. Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) are also important parts of NGDM. In the manufacturing field, VR is mainly applied in designing and testing, while AR is generally applied in assembly. In assembly process, the guidance information of product assembly can be displayed in operators’ view by wearing AR glasses, which is very significant for assisting operators in assembling products [79]. Literature [80] proposed an AR-based re-formable mock-up, which enables interactive changes of shapes of products as well as colors, textures, and user interfaces. Literature [81] proposed a gesture-based AR designing environment where designers can evaluate and modify the designed 3D models through gestures. Besides physical system and information system, human factors are also involved in the process of product manufacturing. The M&S technology can be used to build the human models in the manufacturing system. Interactive simulation between human models and manufacturing resource models can improve the creditability of simulation processes and verify factors related to human. There are both possible challenges and possible opportunities to achieve a better interaction performance between different units for enterprises. At present, the research and application of NGDM is still in its infancy. There are still some key issues need to

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be solved, such as complex time interdependence between units, high decision space of tasks and resources, and models of human beings. 4.2. Model engineering The establishment and management of digital models is an important foundation for manufacturing enterprises to realize digitization of manufacturing systems. Due to the complexity of the manufacturing process, digital models in the manufacturing lifecycle have some new characteristics, including:  The composition of models is more complex. There are more types of constituent components and more complex relationships between model components.  The life cycle of models is longer. In an intelligent manufacturing system, the models will evolve along with the product life cycle. Because of the complexity of the relationship between model components, the model evolution process is very complicated with high uncertainties.  Models are highly heterogeneous. A large number of models are built by different organizations using different platforms, architectures, development languages and databases.  Because of the increased dependence on the models, the model credibility problem becomes more important. But the assessment of the model credibility becomes more difficult as the model complexity increases.  In order to improve the efficiency and quality of model development, the role and value of model reuse is becoming more and more important. For the above reasons, there is an urgent need for a model-oriented theory and method during the lifecycle of complex products. Literature [82] systematically proposed the idea of model engineering, and transformed the model development and management activities from a spontaneous random behavior into a systematic and normative behavior. Model engineering can guarantee the credibility of models at different stages to cope with the challenges encountered in the management of complex system In model engineering, systematic and normative engineering models. methods are used to manage the data, knowledge, activities, and processes involved in the model lifecycle. Although model engineering is proposed for complex system simulation, its

Fig. 4. Digital twin of an industrial robot.

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methods and technologies are applicable to other stages of the manufacturing process. The main research contents of model engineering include: unified modeling language, unified modeling method and tools, model development process management, model maturity assessment, model credibility assessment, model reconstruction and configuration, model composition and reuse, model consistency management and model library management. For new-generation intelligent manufacturing enterprises, models are another type of core resources in addition to data. Effective management and utilization of models is an important basis for enterprises to enhance their innovation capabilities and competitiveness. 4.3. Intelligent cloud simulation With the development of cloud technology, the application of cloud technology in the manufacturing field has gradually become a trend. Cloud manufacturing is an important part of a new generation of intelligent manufacturing [83,84]. Carrying out related manufacturing activities based on cloud platforms is becoming an important means for manufacturing companies to achieve upgrade and transformation. How to support the manufacturing lifecycle through M&S in the cloud environment becomes a new challenge. Based on technical characteristics of cloud computing, virtualization, ubiquitous computing and high-performance computing, literature [85] proposed the concept of cloud simulation which is to realize the on-demand sharing, reuse and collaboration of simulation resources by connecting simulation resources to the cloud simulation platform. Literature [86] reviewed and compared features of the existing modelling and simulation tools in the cloud computing platform. Literature [87] compared different simulators used in cloud computing. Each simulator has its own characteristics which makes it different from others. Literature [88] applied agent-based simulation method to multi-sided platforms and validated the sustainability of the platform ecosystem. Literature [89] applied the artificial neural network to evaluate simulation tasks in the cloud manufacturing platform. Some deep learning resources such as TensorFlow are also applied to enable intelligent processes in modern simulation [90]. Machine learning is also combined with M&S to solve the job shop scheduling problem [91]. The combination of cloud simulation and intelligent manufacturing is becoming an inevitable development trend of manufacturing system simulation, and there are still a lot of problems that need to be studied in depth [92,93], such as: unified modeling of complex manufacturing systems in cloud environment, service-oriented model composition and scheduling, credibility evaluation of simulation models, parallel intelligent optimization algorithm, intelligent cloud simulation platform and simulation computer for manufacturing. 4.4. Industrial big data-based M&S As manufacturing systems become more and more complex, there are mass data generated during the manufacturing lifecycle, that is “Industrial Big Data”. The M&S and industrial big data mutually promote and complement each other. Their powerful combination will effectively promote the development of intelligent manufacturing. On one hand, the emergence of industrial big data brings new opportunities to M&S technology [94]. Traditional M&S methods includes mechanism model by theoretical analysis, empirical model by experiment and statistical model by data. Due to the complexity and uncertainty of manufacturing systems, it is very difficult to use traditional modeling methods to build the models of manufacturing systems. Based on the large amount of data

generated during the system operation, the new model of manufacturing systems can be established by means of machine learning. Although this is a “black box” model, the model can be optimized by continuous learning, so that the model gradually approaches the real system, as shown in Fig. 5. The combination of industrial big data and machine learning technology is an important complement to the M&S approach and facilitates the development of M&S techniques. With the support of industrial big data, the M&S will shift from causal analysis to association analysis [95]. On the other hand, the M&S method plays an important role in acquisition, processing, management and utilization of industrial big data. Industrial big data will be an important research object in the M&S field. Through simulation method, we can explore the value of big data in various manufacturing processes. For example, the big data-based model can be validated and optimized through establishing a virtual simulation environment, and the quality and maturity of the model can then be improved. In addition, simulation technology can also be applied for screening, preprocessing, storage strategy optimization, migration strategy optimization and transmission strategy optimization of big data [96]. 4.5. Edge simulation The Internet of Things (IoT) is the infrastructure of newgeneration intelligent manufacturing systems. Various intelligent manufacturing equipment and sensors in workshops are connected to the enterprise cloud platform through the IoT, so as to realize real-time collection and management of on-site manufacturing data. The cloud platform at the upper end (cloud side) of IoT undertakes the main computing and simulation tasks. However, the execution of manufacturing systems requires high real-time performance. The real-time requirement of the manufacturing system cannot be meet sometimes if we only use the cloud platform to manage and control the whole system. Therefore, corresponding computing and simulation capabilities need to be configured at the lower end (device side) of IoT to improve the real-time performance of the manufacturing system. This is the so-called edge simulation which is a complement and enhance of cloud simulation. The cooperative work between edge simulation and cloud simulation provides more comprehensive support for the management, scheduling and optimization of the production process. 4.6. Embedded simulation In embedded simulation, the simulation system is embedded in the real system and participates in the system operation as shown in Fig. 6. The embedded simulation technology was originally proposed based on requirements of military training. The simulation module was embedded in the real combat equipment to support simulation and training of operators in the real system

Fig. 5. Modeling and simulation with manufacturing big data.

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References

Fig. 6. Interaction among edge simulation, real system and cloud platform.

and improve the simulation experience. In the intelligent manufacturing environment, embedded simulation can support real-time scheduling, on-site monitoring, quality inspection and situation prediction. In addition, embedded simulation can be applied to train workers to use complex manufacturing equipment. Embedded simulation is an extension of edge simulation. Edge simulation and embedded simulation in intelligent manufacturing are both relatively new research directions. There are still some key issues to be solved. 5. Conclusions Based on the new development of information technologies and artificial intelligence technologies, manufacturing systems have evolved from simple single manufacturing equipment to distributed manufacturing systems, and then to the new-generation intelligent manufacturing systems. The complexity of manufacturing systems is also becoming higher and higher. The M&S technology is also becoming more and more important and valuable for the whole manufacturing lifecycle. In consideration of the three different development stages of manufacturing system including unit manufacturing, integrated manufacturing and intelligent manufacturing, this paper summarizes, analyzes and forecasts the research, application status and development trend of manufacturing simulation technologies from aspects of manufacturing unit M&S, manufacturing integrated M&S and manufacturing intelligent M&S. There have been many valuable research results in manufacturing M&S, but key technologies and simulation tools still need to be studied, designed and developed to achieve deeper and wider application of M&S in the new-generation intelligent manufacturing. The application of M&S in manufacturing systems directly affects the innovation ability, R&D efficiency and production quality of the whole manufacturing enterprise. Therefore, manufacturing simulation technologies should receive more attention from enterprises, organizations and governments. In upcoming technologies, some important indispensable factors need to be considered, such as local–global evaluation, interest conflicts in arbitration, human models and efficient adaptation of management of human resources. At present, key technologies of new-generation M&S still need to be further studied to adapt to the coming networked, service-oriented, individualized and intelligent manufacturing environment, such as new-generation digital models, model engineering, cloud simulation, big data-based M&S, edge simulation, embedded simulation, etc. Conflict of interest statement The author declares no conflicts of interest. Acknowledgements This work is supported by the National Natural Science Foundation of China under Grant No. 61873014.

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Barni, A., Montini, E., Menato, S., Sorlini, M., Anaya, V., Poler, R., [88]. Integrating agent based simulation in the design of multi-sided platform business model: a methodological approach. 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, pp. 1–9. Chen, T., Wang, Y.C., [89]. Estimating simulation workload in cloud manufacturing using a classifying artificial neural network ensemble approach. Robotics and Computer-Integrated Manufacturing 38, 42–51. De la Fuente, R., Erazo, I., Smith, R.L., [90]. Enabling intelligent processes in simulation utilizing the TensorFlow deep learning resources. 2018 Winter Simulation Conference (WSC), IEEE, pp. 1108–1119. Zhang, T., Xie, S., Rose, O., [91]. Real-time batching in job shops based on simulation and reinforcement learning. 2018 Winter Simulation Conference (WSC), IEEE, pp. 3331–3339. Shekhar, S., Abdel-Aziz, H., Walker, M., Caglar, F., Gokhale, A., Koutsoukos, X., [92]. A simulation as a service cloud middleware. Annals of Telecommunications 71 (34), 93–108. Higashino, W.A., Capretz, M.A., Bittencourt, L.F., [93]. CEPSim: Modelling and simulation of Complex Event Processing systems in cloud environments. Future Generation Computer Systems 65, 122–139. Tolk, A., [94]. The next generation of modeling & simulation: integrating big data and deep learning. Proceedings of the conference on summer computer simulation, Society for Computer Simulation International, pp. 1–8. Xu, J., Huang, E., Chen, C.H., Lee, L.H., [95]. Simulation optimization: A review and exploration in the new era of cloud computing and big data. Asia-Pacific Journal of Operational Research 32 (03) p. 1550019. Babiceanu, R.F., Seker, R., [96]. Big Data and virtualization for manufacturing cyberphysical systems: A survey of the current status and future outlook. Computers in Industry 81, 128–137. Lin Zhang is a professor of Beihang University. He received the B.S. degree in 1986 from Nankai University, and received the M.S. degree and the Ph.D degree in 1989 and 1992 from Tsinghua University, China. He served as President of the Society for Modeling and Simulation International (SCS) (2015–2016). He is a Fellow of SCS, a Fellow of ASIASIM, executive vice president of China Simulation Federation, IEEE senior member, associate Editor-in-Chief and associate editors of six peer-reviewed international journals. His research interests include modeling and simulation, cloud manufacturing and model engineering.

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Longfei Zhou is a research scholar in the Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology. He received B.S. degree from Harbin Engineering University in 2012 and received Ph.D degree from Beihang University in 2018. His research interests include manufacturing, scheduling, modeling and simulation, machine learning and computer vision. He received the Outstanding Graduate Award granted by the Beijing Municipal Education Commission in 2018.

Lei Ren is an associate professor at School of Automation Science and Electrical Engineering in Beihang University. He received the Ph.D. degree in computer science from the Institute of Software, Chinese Academy of Sciences in 2009. He has published more than 50 papers and a book. He served as an associate editor of SIMULATION: Transactions of the Society for Modeling and Simulation International, and reviewers for journals. His research interests include big data analytics and applications.

Yuanjun Laili is an assistant professor at School of Automation Science and Electrical Engineering in Beihang University. She received the Ph.D. degree at Beihang University. Her research interests include cloud manufacturing and evolutionary optimization.