Digital twins-based smart manufacturing system design in Industry 4.0: A review

A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As an emerging technology, digital twins can achieve semi-physical simulations to reduce the vast time and cost of physical commissioning/reconfiguration by the early detection of design errors/flaws of the SMS. However, the development of the digital twins concept in the SMS design remains vague. An innovative Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework is proposed to review how digital twins technologies are integrated into and promote the SMS design based on a literature search in the Web of Science database. The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented in this survey. It is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM)

Future factories will feature strong integration of physical machines and cyber-enabled software, working seamlessly to improve manufacturing production efficiency. In these digitally enabled and network connected factories, each physical machine on the shop floor can have its ‘virtual twin’ available in cyberspace. This ‘virtual twin’ is populated with data streaming in from the physical machines to represent a near real-time as-is state of the machine in cyberspace. This results in the virtualization of a machine resource to external factory manufacturing systems. This paper describes how streaming data can be stored in a scalable and flexible document schema based database such as MongoDB, a data store that makes up the virtual twin system. We present an architecture, which allows third-party integration of software apps to interface with the virtual manufacturing machines. We evaluate our database schema against query statements and provide examples of how third-party apps can interface with manufacturing machines using the VMM middleware. Finally, we discuss an operating system architecture for VMMs across the manufacturing cyberspace, which necessitates command and control of various virtualized manufacturing machines, opening new possibilities in cyber-physical systems in manufacturing.     

Digital twin-based designing of the configuration,motion, control,and optimization model of a flow-type smart manufacturing system

Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient.     

Analysis of fractal manufacturing systems framework towards industry 4.0

Currently, ways to approach the design of Cyber Physical Systems in Industry 4.0 are under development. Emerging concepts of Smart Factories require the development of specialized knowledge; new working methods are also needed to manage the transition from conventional industry to industry 4.0. To achieve this objective, fractal theory could provide the appropriate knowledge and tools. Fractal systems applied to manufacturing have been widely used over the last decades to design complex adaptive systems: it allows the introduction of resilience requirements (capacity to react to changes in a turbulent environment) and to reduce the complexity of its structure, operation and management. In order to know the potential and the possibility of applying fractal theory to the design of systems in Industry 4.0, this article reviews the publications that develop fractal systems for manufacturing engineering. The review includes contributions published between 1985 (approximate date of the first works on the theory applied to manufacturing engineering) and 2019. The objective is to gather those strategies, methodologies and successful case studies that can be useful for the approaches of Industry 4.0 and to define a set of future lines of work for the adaptation of the fractal theory to the new challenges posed by Industry 4.0.     

A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.     

Toward a cloud-based manufacturing execution system for distributed manufacturing

This paper illustrates the needs and challenges for the management of distributed manufacturing in a multi-company supply chain and processes these further as features of new IT systems. Requirements are collected from manufacturing companies and combined with insights from literature in the field of current ERP/MES system drawbacks, advantages, needs and challenges. The findings show that the needs and challenges in data integration inside SME networks are closely related to the limitations of current supply chain solutions. Current ERP-solutions lack extended enterprise support and a shared cloud-based approach. On the other hand, current MES solutions can operate the manufacturing process, but not for distributed manufacturing. As an answer to the requirements, we made a proposal for the core of architecture for next generation of MES solution in this position paper. Moreover, a pilot software tool has been developed to support the needs related to real time, cloud-based, light weight operation.     

An intelligent virtual metrology system with adaptive update for semiconductor manufacturing

Virtual metrology involves the estimation of metrology values using a prediction model instead of metrological equipment, thereby providing an efficient means for wafer-to-wafer quality control. Because wafer characteristics change over time according to the influence of several factors in the manufacturing process, the prediction model should be suitably updated in view of recent actual metrology results. This gives rise to a trade-off relationship, as more frequent updates result in a higher accuracy for virtual metrology, while also incurring a heavier cost in actual metrology. In this paper, we propose an intelligent virtual metrology system to achieve a superior metrology performance with lower costs. By employing an ensemble of artificial neural networks as the prediction model, the prediction, reliability estimation, and model update are successfully integrated into the proposed virtual metrology system. In this system, actual metrology is only performed for those wafers where the current prediction model cannot perform reliable predictions. When actual metrology is performed, the prediction model is instantly updated to incorporate the results. Consequently, the actual metrology ratio is automatically adjusted according to the corresponding circumstances. We demonstrate the effectiveness of the method through experimental validation on actual datasets.     

Using augmented reality to build digital twin for reconfigurable additive manufacturing system

The extrusion-based additive manufacturing (AM) processes are those where one or multiple tools (usually nozzles) are driven along predefined paths to deposit fabrication materials. They are usually inherently slow because solid contours have to be filled with mere single deposition lines of material. An intuitive way to improve the fabrication speed is to introduce multiple independent actuators for concurrent deposition of materials without collision among them. In this paper, a methodology of using augmented reality (AR) technique is presented to conveniently communicate the layout information between a reconfigurable AM system made of robotic arms and its corresponding digital twin for toolpath planning and simulation. A prototype system made of two desktops AM robotic arms is developed, and transformation matrices are derived to determine the spatial relation between different items in the system, including camera, markers, robotic arms and part substrate. Case studies are conducted to demonstrate the capability of this methodology in automatically retrieving layout information and assisting users to deploy pre-determined layout. The results show that the developed methodology enables rapid retrieval of position information from the physical system layout into the digital twin simulation and optimization and facilitates convenient deployment of an optimized layout determined in the digital twin into the physical system.     

An object-oriented framework for developing distributed manufacturing architectures

Management of complexity, changes and disturbances is one of the key issues of production today. Distributed, agent-based structures represent viable alternatives to hierarchical systems provided with reactive/proactive capabilities. In the paper, approaches to distributed manufacturing architectures are surveyed, and their fundamental features are highlighted, together with the main questions to be answered while designing new structures. Moreover, an object-oriented simulation framework for development and evaluation of multi-agent manufacturing architectures is introduced.     

制造执行系统柔性应用框架研究

针对制造执行系统应用开发的复杂性,提出了一个制造执行系统的柔性应用框架。在多层服务体系结构的基础上,该应用框架通过运用面向过程的对象分析技术、基于规则的事件服务机制和业务工程分析技术,提供可重构的组织结构、可伸缩的业务流程和可定制的业务规则,使得系统开发和实施的柔性得以提高。同时,运用该柔性框架,对一个实例进行分析和实现。     

A power system nonlinear adaptive decentralized controller design

In this paper, a novel excitation control is designed for improvement of transient stability of power systems. The control algorithm is based on the adaptive backstepping method in a recursive way without linearizing the system model. Lyapunov function method is applied in designing the controller to ensure the convergence of the power angle, relative speed of the generator and the active electrical power delivered by the generator when a large fault occurs. Compared with the existing nonlinear decentralized control approaches, the proposed controller has no requirement for the bounds of interconnections in the power system. And the new approach does not need the existence of solution of a designed algebraic Riccati equation. Furthermore, the transient stability performance of power systems can also be improved by the designed control approach. The efficacy of the designed controller has been demonstrated in a multimachine power system. Simulation results show transient stability enhancement of a power system in the face of a large sudden fault.     

A dynamic scheduling holon for manufacturing orders

This paper deals with a new architecture and negotiation protocol for the dynamic scheduling of manufacturing systems. The architecture is based on two paradigms: multi-agent systems and holonic systems. The main contribution in the architecture is the existence of holons representing tasks together with holons representing resources. The well-known contract net protocol has been adapted to handle temporal constraints and to deal with conflicts. It also deals with conflict situations, namely with the case of the indecision problem. This approach assumes that deadlines are the most important constraints to consider.     

An information system for computer-integrated manufacturing systems

One of the main problems when implementing the computer-integrated manufacturing (CIM) concept concerns information integration. In order to support information integration, an information system provided with suitable data models is required. In this paper an information system is presented, which fulfils the requirements for an appropriate information management in CIM. For this purpose, an EXPRESS-oriented information system has been built on top of a commercial relational DBMS. The conceptual model of the information is built in EXPRESS and then parsed and translated to the corresponding relational constructs. A data access interface has been implemented which allows EXPRESS-oriented data manipulation. The information system was developed inside ESPRIT project No. 2202 CIM-PLATO “CIM System Planning Toolbox” to integrate the information used by the tools developed for flexible manufacturing system planning.     

An alternate stability analysis framework for adaptive control

A proof is given for the uniform asymptotic stability of a class of pointwise-in-time stable systems of time-varying ordinary differential equations. The sufficient conditions for stability are shown to be naturally testable in a general adaptive control context. A weak form of robust identification is found to imply robust stability of the overall closed-loop adaptive control system.     

An innovative approach for resource sharing and scheduling in a sustainable distributed manufacturing system

Secure, transparent, and sustainable distributed manufacturing system (DMS) is a pressing need for current Industry 4.0. In this paper, exchange of highly sensitive information in a more transparent and secure way and to avoid the misunderstandings and trust issues between the enterprises a smart contract based on blockchain technology has been proposed in case of a distributed manufacturing environment. Here, we used a public-permission less Ethereum platform to execute the smart contracts in the Blockchain to process the customer orders and to identify the right enterprise. Later, a multi-objective mixed-integer linear programming (MILP) model is formulated for optimal resource sharing and scheduling in a considered sustainable DMS. The objectives of the proposed model consist of simultaneously improvement of the performance measures such as makespan, machine utilization, energy consumption, and reliability. To solve this MILP model, a new Multi-objective-based Hybridized Moth Flame Evolutionary Optimization Algorithm (HMFEO) is developed and then the effectiveness of the proposed algorithm is validated with the Non-dominated Sorting Genetic Algorithm (NSGA-III). The results obtained from implementing the model using experimental data along with different cases show the efficiency and the validity of the proposed model and solution approach. Moreover, several performance indicators like hyper volume are increased by nearly 15–20 % that shows the superiority of the proposed algorithm with the NSGA-III.     

Algorithm for the design of single-stage adaptive kanban system

The traditional kanban system with fixed number of cards does not work satisfactorily in unstable environment. In the adaptive kanban-type pull control mechanism the number of kanban is allowed to change with respect to the inventory and backorder level. It is required to set the threshold values at which cards are added or deleted which is a part of the design. Previous studies used the local search method to design the adaptive kanban system. In this paper Genetic Algorithm- and Simulated annealing-based heuristics are developed and used to set the design parameters of adaptive kanban system. The numerical results indicate that simulated annealing based heuristics produces better solution with improved computational efficiency.     

A guide to the design offuzzy control systems for manufacturing processes

While classical control theory has been demonstrated to be highly successful in many manufacturing technology applications, there are shortcomings when applied to processes that require the intuitive skills of a human operator. Fuzzy logic technique can be a significant aid in enabling machine systems to imitate the control stategy of an operator and so achieve an efficient control function. Commencing with the basic principles of fuzzy logic theory, the paper provides a practical guide to the design techniques used to establish fuzzy controller. An example of a welding robot to achieve an irregular weld path profile is used to illustrate the procedure.     

Multi-agent system and reinforcement learning approach for distributed intelligence in a flexible smart manufacturing system

Personalized production has emerged as a result of the increasing customer demand for more personalized products. Personalized production systems carry a greater amount of uncertainty and variability when compared with traditional manufacturing systems. In this paper, we present a smart manufacturing system using a multi-agent system and reinforcement learning, which is characterized by machines with intelligent agents to enable a system to have autonomy of decision making, sociability to interact with other systems, and intelligence to learn dynamically changing environments. In the proposed system, machines with intelligent agents evaluate the priorities of jobs and distribute them through negotiation. In addition, we propose methods for machines with intelligent agents to learn to make better decisions. The performance of the proposed system and the dispatching rule is demonstrated by comparing the results of the scheduling problem with early completion, productivity, and delay. The obtained results show that the manufacturing system with distributed artificial intelligence is competitive in a dynamic environment.