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Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system's life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs). [ABSTRACT FROM AUTHOR]

The global trend in manufacturing has shifted from a manufacturing-centric process toward a user-centric process. This has resulted in a shorter lifespan and a high product replacement rate of any consumer product. Germany has introduced the concept of Industry Revolution 4.0 (IR 4.0) to convert manufacturing processes and mechanisms into cyber-physical systems (CPS). Digital factory, being the first step into CPS and IR4.0, is being targeted as the most important evolution of the manufacturing industry. This paper defines digital factories and their differences between other similar domains such as smart factories, CPS, and virtual factories. The requirements and goals of a digital factory are explained in detail to facilitate future digital factory tool developments. Furthermore, the current challenges faced in the implementation of the digital factory are proposed to be approached by adapting an interoperable virtual reality technology. This paper emphasizes the usage of virtual reality (VR) in simulating a digital factory that aids in the decision-making and efficient operation of a manufacturing facility. Furthermore, recommendations gathered from previous studies for developing VR-based digital factory tools are also explained in detail in this paper. [ABSTRACT FROM AUTHOR]

Personalized production is a manufacturing concept relevant to the fourth industrial revolution, which can satisfy various customer needs inexpensively. There are three main hurdles to the efficient implementation of this concept: access, cost, and performance. This paper proposes a digital twin-based cyber physical production system (CPPS) architectural framework that overcomes the performance hurdle. The proposed architectural framework comprises five services that are solutions to the performance hurdle of personalized production, and it operates on information based on the proposed product, process, plan, plant, and resource (P4R) information model. This information model is manufacturing abstraction for personalized production and is presented on a detailed level with object-orientation and the "type and instance" concept. Whereas previous studies on the digital twin concept considered one or several facilities and focused on the development of independent applications, this study focuses on the digital twin as a core technological element of the entire system and analyzes CPPS design and its operation from the system-of-systems perspective. The application of the digital twin-based CPPS to a micro smart factory (MSF) provides an advanced solution for the personalized production of various products. An average makespan improvement of ~ 26.87% was achieved using the implemented CPPS services in the MSF. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry. [ABSTRACT FROM AUTHOR]

With the rapid advent of new information technologies (Big Data analytics, cyber-physical systems, such as IoT, cloud computing and artificial intelligence), digital twins are being used more and more in smart manufacturing. Despite the fact that their use in industry has attracted the attention of many practitioners and researchers, there is still a need for an integrated and comprehensive digital twin framework for reconfigurable manufacturing systems. To close this research gap, we present evidence from a systematic literature review, including 76 papers from high-quality journals. This paper presents the current research trends on evaluation and the digital twin in reconfigurable manufacturing systems, highlighting application areas and key methodologies and tools. The originality of this paper lies in its proposal of interesting avenues for future research on the integration of the digital twin in the evaluation of RMS. The benefits of digital twins are multiple such as evaluation of current and future capabilities of an RMS during its life cycle, early discovery of system performance deficiencies and production optimization. The idea is to implement a digital twin that links the virtual and physical environments. Finally, important issues and emerging trends in the literature are highlighted to encourage researchers and practitioners to develop studies in this area that are strongly related to the Industry 4.0 environment. [ABSTRACT FROM AUTHOR]

Rapid tool wear is a major concern of the machining operation, affecting the tooling cost and dimensional tolerance of the components. In line with Industry 4.0, rapid tool failure can be avoided by applying cyber-physical tool condition monitoring (TCM), which detects in-process tool wear evolution using sensors or machine vision systems, determining the actual time for tool replacement. Although sensor-based TCM is quick and adaptive in monitoring tool wear progression online, it cannot detect the failure modes to show the extent of wear severity on the tool's cutting edge. On the other hand, machine vision systems effectively detect wear mechanisms that accelerate tool failure during machining. Therefore, this paper presents the practical application of machine vision systems in TCM and tool performance optimization (TPO). The findings in this research show that digital microscopes are used to monitor wear mechanisms, complementing TPO techniques in selecting the best cutting parameters that optimize tool performance. However, such techniques are time intensive and inefficient for real-time applications. With recent advances in imaging technology and artificial intelligence, an in-process machine vision-based TCM (MV-TCM) system is receiving more attention in intelligent manufacturing due to its efficient predictive capability. However, it is still in its infancy stage, relying on classical machine learning models, which are ineffective to extract high-level features on the tool wear images for in-process failure modes detection. Therefore, this paper highlights the significance of applying artificial intelligence to enhance MV-TCM capability for online failure modes detection and classification. [ABSTRACT FROM AUTHOR]

As a highly secure and reliable system, aviation faces challenges including digital transformation, high production operation costs, low maintenance efficiency, and intensive technology. One of the most beneficial technologies is the digital twin (DT), which can solve the above issues. This paper investigates aviation industry DT from government authorities, industry, and academia. Firstly, it surveys the development of the definition of DT and compares its concepts with the Internet of Things, cyber-physical systems, and digital thread etc. Next, it shows the research timeline of aviation DT in the past 10 years by authorities such as Air Force Research Laboratory and National Aeronautics and Space Administration. Then, this paper reviews the state-of-the-art research status of DT in the whole lifecycle of the aviation system and concludes that aviation DT is the most widely used in manufacturing and maintenance, and should pay attention to the application of UAV DT. Finally, it summarizes that data fusion, high-fidelity modeling, integration with New IT, and human–machine interaction are the key technologies of aviation DT. The future possible research directions could be digital process twin risk control, interactive DT, and DT data learning. [ABSTRACT FROM AUTHOR]

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

Production systems play an important role in intelligent manufacturing. A large number of manufacturing resources are designed and developed with virtual (digital) ones, which will be associated with the physical ones throughout their lifecycle. With the recent emergence of information and communications technologies (ICTs), such as internet of things, big data, virtual reality, artificial intelligence, and 5G, the interconnection and interaction between physical resources and virtual ones become possible in production systems. Digital twin (DT) shows great potential to realize the cyber-physical production system (CPPS) in the era of Industry 4.0. In this paper, we present our vision on integrating various physical resources into CPPS via DT and AutomationML. To elaborate on how to apply ICTs, this paper firstly explores a generic architecture of CPPS based on DT. DT is a virtual and authoritative representation of physical manufacturing resource, since DT includes various models and manufacturing big data of resource. The proposed architecture is illustrated in detail as follows: (1) physical layer, (2) network layer, (3) virtual layer, and (4) application layer. A case of expert fault diagnose for aircraft engine is presented using the proposed information fusion in the architecture. Secondly, this paper proposes an approach of information modeling for CPPS based on AutomationML. Various manufacturing services can be encapsulated and defined in the standardized format (AutomationML), and then the corresponding virtual manufacturing resources (DTs) will be integrated into CPPS. Finally, this paper describes a case of information modeling for blisk machining and demonstrates the modeling approach in real-life scenarios for support manufacturing resource sharing via DT. Furthermore, the conclusion and further work is briefly summarized. [ABSTRACT FROM AUTHOR]

The advances in Industry 4.0 provide both challenges and opportunities for digital manufacturing and assembly systems. This paper first addresses the state-of-the-art readiness for Industry 4.0 concerning assembly and manufacturing systems through a literature review of the relevant papers recently published. Then it assesses the challenges faced nowadays by assembly and manufacturing systems. Third, it focuses on the most promising future developments and evolution of such production systems as well as their digitalisation. Finally, this manuscript illustrates the content of the papers selected for this special issue. Through the study presented in this special issue, valuable contributions to both theory and application in this area have been achieved, and a useful reference for future research is given. [ABSTRACT FROM AUTHOR]

Industry 4.0 and its related technologies (e.g., embedded sensing, internet-of-things, and cyber-physical systems) are promising a paradigm shift in manufacturing automation. However, with a continual increase in device interconnectivity, securing these systems becomes crucial. As these systems evolve, opportunities for cyberattacks extend to include attacks that can physically alter parts (Product-Oriented C2P attacks). Fortunately, since these cyber-physical attacks affect the physical world, there exists potential to detect an attack through its physical manifestation. Typically, in manufacturing, quality control (QC) systems are used to detect quality losses or deviations from nominal. This paper proposes that QC tools can be adapted to act as physical detection layers as part of a defense-in-depth strategy (common IT security strategy) that increases the difficulty/cost required for a successful attack. However, effectively designing physical detection layers requires understanding the extent to which attacks can (and cannot) be designed to avoid detection. In response, this paper proposes a machining specific attack design scheme and an attack design designation system (ADDS) that provides the structure to populate a wide variety of potential attacks. To illustrate the importance of applying a defense-in-depth strategy for machining, a case study is conducted with several realistic attacks against an example machining process that collects in-situ process data. Within this case study, the proposed ADDS is employed to systematically describe how these attacks could be designed to avoid detection. Finally, through this exploration, this paper shows how employing process-domain knowledge to understand the effects of Product-Oriented attacks on process physics can further aid in detection layer designs. [ABSTRACT FROM AUTHOR]

The paper presents the main approaches to digital twin formation taking into account many years of research. A concept for the creation of digital twins is proposed, with regard to the evolution of the current state of the object. A digital twin is an innovative concept that is becoming increasingly relevant with the development of the fourth industrial revolution and the transition to intelligent manufacturing. The paper provides an analysis of works in the field of digital twins, considers specific features of the concept application in manufacturing, and analyzes the main problems associated with its practical implementation and their possible solutions. The work places particular emphasis on the features of the multiphysics model formation, the specifics of working with large arrays of heterogeneous data for digital twins, and the requirements for cyber-physical technological systems that are their prototypes. [ABSTRACT FROM AUTHOR]

With the development and application of advanced technologies such as Cyber Physical System, Internet of Things, Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., more manufacturing enterprises are transforming to intelligent enterprises. Smart manufacturing systems (SMSs) have become the focus of attention of some countries and manufacturing enterprises. At present, there are some applications of SMSs in different industrial fields. However, there is still a lack of a unified definition of SMSs and a unified analysis of requirements. In order to have a comprehensive understanding of SMSs, this paper summarized the evolution, definition, objectives, functional requirements, business requirements, technical requirements, and components of SMSs. At the same time, it points out the current development status and level. Based on above, an autonomous SMSs model driven by dynamic demand and key performance indicators is proposed. Through the review of this paper, the reference can be provided for the transformation of more manufacturing enterprises from the traditional to the intellectualized ones. [ABSTRACT FROM AUTHOR]

Industry 4.0 is characterized by a modular structure of the production process that consists of cyber-physical systems. These cyber-physical systems provide interoperability, information transparency, and decentralization of decisions. The modular structure, according to Industry 4.0 principle, creates intelligent networks of machines, work pieces, and systems that can predict failures, self-organize themselves, and react to unexpected events. In this paper, we consider the complexity of assembly processes and propose modular structures for assembly processes based on probabilistic formulation. Despite the reliability and precisions that the use of cyber-physical systems such as robotics and automation in assembly processes have introduced, and because of the increasing complexity, there is a need for probabilistic process characterization models for smart assembly planning purposes. First, a new framework for assembly complexity measurement based on processes' probabilistic and Markovian characters is suggested. Then, two effects of modularization, namely stabilization of components by boundary creation and application modular interfaces, are analyzed. For each case, a probabilistic formulation for assembly formation and analysis is presented. The effect of task sequencing and component modularization on assembly time and cost is considered simultaneously by the Bayesian formulation of the assembly problem. Several heuristics are derived from simulation examples, and the modularization cost is studied through utilization of design structure matrix. [ABSTRACT FROM AUTHOR]

This paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case. [ABSTRACT FROM AUTHOR]

Controlling and dosing electrolytes is a key challenge in the operation of electroplating process chains. Electrolyte components are continuously degraded and dragged out during the production process. This process is influenced by a variety of internal and external factors such as process parameters, the electrolyte itself, anodes, the substrates and the production environment. The exact analytical measurement of the electrolyte composition requires extensive analytical equipment and typically cannot be completely realized within an industrial plating company. Therefore, this paper presents a model-based approach, integrated in a cyber-physical production system, for controlling and dosing electrolytes. A mathematical resource flow model is the basis for a dynamic agent-based simulation. This model uses available data from the manufacturing execution system and enterprise resource planning system to model the current composition of the electrolyte. The approach is successfully validated for two different electrolyte substances at an industrial acid zinc–nickel barrel plating process chain for automotive parts. [ABSTRACT FROM AUTHOR]

This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes the user's declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration. [ABSTRACT FROM AUTHOR]

Cyber-physical production systems (CPPS) are mechatronic systems monitored and controlled by software brains and digital information. Despite its fast development along with the advancement of Industry 4.0 paradigms, an adaptive monitoring system remains challenging when considering integration with traditional manufacturing factories. In this paper, a failure predictive tool is developed and implemented. The predictive mechanism, underpinned by a hybrid model of the dynamic principal component analysis and the gradient boosting decision trees, is capable of anticipating the production stop before one occurs. The proposed methodology is implemented and experimented on a repetitive milling process hosted in a real-world CPPS hub. The online testing results have shown the accuracy of the predicted production failures using the proposed predictive tool is as high as 73% measured by the AUC score. [ABSTRACT FROM AUTHOR]

With the rapid development of smart manufacturing, some challenges are emerging in the production management, including the utilization of information technology and the elimination of dynamic disturbance. A digital twin-driven production management system (DTPMS) can dynamically simulate and optimize production processes in manufacturing and achieve real-time synchronization, high fidelity, and real-virtual fusion in cyber-physical production. This paper focuses on establishing DTPMS for production life-cycle management. First, we illustrate how to integrate digital twin technology and simulation platforms. Second, a framework of DTPMS is proposed to support a cyber-physical system of production workshop, including product design, product manufacturing, and intelligent service management. Finally, the proposed DTPMS is applied to the production process of a heavy-duty vehicle gearbox. The experimental results indicate that the defective rate of products and the in-process inventory are reduced by 34% and 89%, respectively, while the one-time pass rate of product inspection is increased by 14.2%, which demonstrates the feasibility and effectiveness of the DTPMS. [ABSTRACT FROM AUTHOR]

In recent years, the nations of the world have presented the development strategy of manufacturing. Manufacturing is the foundation of a country. However, with the increasing global resource constraints and market heterogeneity, the variety of individual demands, and the long-term goals of sustainable development, with the support of emerging information and communication technologies such as Internet, cyber-physical system, Internet of Things, cloud computing, and big data, industrial value creation is causing a paradigm shift in manufacturing. This paper studies a range of new manufacturing paradigms and presents a state-of-the-art survey of published works. It explores the corresponding current manufacturing concepts, technologies, framework features, application effects, resource optimization, and future challenges in these new paradigms. The integration of various manufacturing paradigms is also analyzed. Through this survey, the developments of these new manufacturing paradigms are explained and the future prospects are also discussed. [ABSTRACT FROM AUTHOR]

Industry 4.0, digitization, and Internet of Things enable companies to react quickly and flexibly to market changes. The objective is the production of customized products at the cost of a mass-produced product. The widespread entry of cyber-physical systems enables a networking of all entities. The introduction of cyber-physical systems in production and logistic systems dissolves existing rigid structures and as a result versatile cyber-physical production systems occur. To support the flexibility of the cyber-physical production systems, the production supply needs to be adapted. For this reason, this paper proposes a novel universal production supply concept. This concept introduces a decentralized controlled supply. It is executed by several cyber-physical system entities, which are represented by software agents. These agents negotiate autonomously with each other. The novel concept was implemented in a research lab and evaluated quantitatively and qualitatively. For the quantitative evaluation, the efficiency of the novel concept is evaluated and compared with a kanban supply. An innovative key performance indicator system called process status indicators evaluates the efficiency. The result of this indicator system states that the novel concept is more efficient than the kanban supply. [ABSTRACT FROM AUTHOR]

The emergence of Cyber Physical System has dramatically impacted the use of traditionally centralized control system in responding to unexpected events. Rush order is a quite common unexpected event in the current dynamic market characteristics and has significant perturbing ability to a centrally predictive schedule. This paper is aimed to propose a consensus algorithm for multi-agent-based manufacturing system (CoMM) to control the rush order and henceforth minimize a makespan. Consensus is an algorithmic procedure applied in control theory which allows convergence of state between locally autonomous agents collaborating for their common goal. Leader-follower communication approach was used among the multi-agent to deal with the perturbing event. Each agent decides when to broadcast its state to neighbor agents, and the controlling decision depends on the behavior of this state. The consensus algorithm is initially modeled by networking all contributing agents. After this, it is validated with simulation experiment based on academic full-sized ap plication platform called TRACILOGIS platform. The results showed that the consensus algorithm has significantly minimized the impact of rush order on makespan of manufacturing orders launched on a system. [ABSTRACT FROM AUTHOR]

Assembly systems constitute one of the most important fields in today's industry. The need for flexibility in the assembly process imposes several challenges in the development of this kind of systems. Model-driven engineering (MDE) has been successfully used to alleviate the complexity of platforms and express domain concepts effectively. In this paper, an open distributed architecture for the engineering of evolvable flexible hybrid assembly systems is described. The proposed architecture is based on the model-driven development paradigm. Models are used to represent in a formalized way the structure, the behavior, and the requirements of the assembly systems. A domain-specific engineering tool is defined to facilitate the assembly system engineer in the engineering process of assembly systems. Specific meta-models are defined to capture domain knowledge and guide the engineer in the construction of the models required to construct the assembly system. The proposed approach imposes a correct-by-construction approach and is based on an architecture that exploits the benefits of Cyber Physical paradigm utilizing web technologies, Internet of Things, and Cloud computing. [ABSTRACT FROM AUTHOR]

Digital twin technology is considered as a key technology to realize cyber-physical systems (CPS). However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little progress has been achieved in digital twin application, especially in the complex product assembly shop-floor. In this paper, we propose a framework of digital twin-based smart production management and control approach for complex product assembly shop-floors. Four core techniques embodied in the framework are illustrated in detail as follows: (1) real-time acquisition, organization, and management of the physical assembly shop-floor data, (2) construction of the assembly shop-floor digital twin, (3) digital twin and big data-driven prediction of the assembly shop-floor, and (4) digital twin-based assembly shop-floor production management and control service. To elaborate how to apply the proposed approach to reality, we present detailed implementation process of the proposed digital twin-based smart production management and control approach in a satellite assembly shop-floor scenario. Meanwhile, the future work to completely fulfill digital twin-based smart production management and control concept for complex product assembly shop-floors are discussed. [ABSTRACT FROM AUTHOR]

In today’s dynamic manufacturing environments, the adoption of virtual reality (VR)-based simulation technologies to help in product and process design activities is becoming more widespread. With the onset of the next IT-oriented revolution involving global cyber manufacturing practices, the recent emergence of Internet of things (IoT)-related technologies holds significant promise in ushering an era of seamless information exchange which will provide a robust foundation for the next generation of smart manufacturing frameworks dependent on cyber physical system (CPS)-based principles, approaches, and technologies. In this paper, we present a broad framework for IoT-based collaborations involving the adoption of VR-based analysis environments networked with other cyber physical components. The process context for this VR-centered approach is electronics assembly, which involves the assembly of printed circuit boards. In such manufacturing contexts, it is essential to have a seamless flow of data/information among the various cyber physical components to ensure an agile collaborative strategy which can accommodate changing customer needs. VR-based simulation environments play a key role in this framework which supports multiple users collaborating using haptic interfaces and next-generation network technologies. The simulation outcomes and production data from physical shop floors can be compared and analyzed using this IoT framework and approach. [ABSTRACT FROM AUTHOR]

Nowadays, along with the application of newgeneration information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively. [ABSTRACT FROM AUTHOR]

The vision of smart shop floor is based on the notion of Industry 4.0 that denotes technologies and concepts related to Cyber-Physical Production Systems (CPPS). In smart shop floors, CPPS monitors physical processes, creates a virtual copy of the physical world, and makes decentralized decisions. CPPS allows virtual world to store data, process data, communicate, and cooperate with each other in real time. This paper presents architecture of smart shop floor based on physical, logical, and communication layers that embed intelligent approaches within manufacturing processes. Every physical entity in the smart shop floor is regarded as an autonomous intelligent logical unit that performs operations guided by distributed control functions. Moreover, computing power and optimization approaches are embedded into each logical unit to make decisions to agilely respond to frequent occurrence of unexpected disturbances at the shop floor. A test platform has been set up to demonstrate how physical entities can be cooperative and autonomous logical units that can automatize the shop floor operations. The results verify the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The growing complexity of production systems requires appropriate control architectures that allow flexible adaptation during their runtime. Although cyber-physical production systems (CPPS) provide the means to cope with complexity and flexibility, the migration with existing control systems is still a challenge. The term CPPS denotes a mechatronic system (physical world) coupled with software entities and digital information (cyber part), both enabling the smart factory concept for the Industry 4.0 (I4.0) paradigm. In this regard, design patterns could help developers to build their software with common solutions for manufacturing control derived from experiences. We provide a description and comparison of the already existing multi-agent systems (MAS) design patterns, which were collected and classified by introducing two classification criteria to support MAS developers. The applicability of these criteria is shown in the case of specific example architectures from the lower and higher control levels. The authors, together with experts from the German Agent Systems committee FA 5.15, gathered more than twenty MAS patterns, evaluated, and compared four selected patterns with the presented criteria and terminology. The main contribution is a CPPS architecture that fulfills requirements related to the era of smart factories, as well as the Reference Architectural Model I4.0 (RAMI 4.0). The conclusions indicate that agent-based patterns greatly benefit the CPPS design. In addition, it is shown that manufacturing based on MAS is a good way to address complex requests of the CPPS development. [ABSTRACT FROM AUTHOR]

When cyber-physical systems (CPS) are connected to perform smart manufacturing, we have a cyber-physical production system (CPPS). In such systems, CPS collaborate in an intelligent way to obtain and maintain the optimum of the manufacturing process, handle disturbances and adapt to changing conditions. This work, based on a systematic literature review, shows current trends in cyber-physical production systems with a special focus on the role of connectivity and control systems in production. We are looking for the enablers of CPPS focusing on the current evolution of control systems in the smart factory of the future. As a result, a conceptual framework as well as a CPPS control system architecture for the introduction and organisation of enablers for CPPS is presented to support practitioners in the realisation of smart manufacturing control concepts. [ABSTRACT FROM AUTHOR]

Fused deposition modeling (FDM) has been widely applied in the automotive, aerospace, industrial, and medical fields in recent years. However, residual stress and part deformation caused by thermal effects are still significant issues that limit the development of the technology. Improving the temperature control system has been shown to be an efficient way to reduce thermal effects. In this work, nozzle temperature and platform temperature were first studied to provide experimental data for modifying a control system. Identical parts were printed using different settings and distortion was measured for each part. Then, based on the distortion data, a cyber-model was built to predict deformation based on printing settings. The prediction test results show that a linear regression model outperformed an artificial neural network model and a support vector machine model. Based on the linear regression model, a cyber-physical system (CPS) was built to adjust nozzle temperature settings automatically. A performance evaluation experiment showed that this CPS system can reduce the distortion significantly. In future research, printing speed control and platform temperature control will also be included in the CPS system to further decrease the distortion. [ABSTRACT FROM AUTHOR]

The advent of smart manufacturing and the exposure to a new generation of technological enablers have revolutionized the way manufacturing process is carried out. Cyber-Physical Production Systems (CPPS) are introduced as main actors of this manufacturing shift. They are characterized for having high levels of communication, integration and computational capabilities that led them to a certain level of autonomy. Despite the high expectations and vision of CPPS, it still remains an exploratory topic. Multi-Agent Systems (MAS) have been widely used by software engineers to solve traditional computing problems, e.g., banking transactions. Because of their high levels of distribution and autonomous capabilities, MAS have been considered by the research community as a good solution to design and implement CPPS. This work first introduces a collection of requirements and characteristics of smart manufacturing. A comprehensive review of various research applications is presented to understand the current state of the art and the application of agent technology in manufacturing. Considering the smart manufacturing requirements and current research application, a SWOT analysis was formulated which identifies pros and cons of the implementation of agents in industry. The SWOT analysis was further validated by an industrial expert evaluation and the main findings and discussion of the results are presented. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a key enabling technology to realize cyber-physical system (CPS), which can truly perceive, map, and predict the operating state of physical entity. Through analyzing the generality of the testing process of large and complex equipment components (LCEC), a five-dimensional framework of DT-based test process management (DTTPM) is proposed, which comprises physical layer, network layer, data layer, model layer, and service layer. In order to realize the visualization and enhance the controllability, security, and information transparency of LCEC in the testing process, three key technologies are elaborated in detail as follows: (1) the construction of the twin semantic model of the testing process in model layer, (2) the synchronization method of twin model and physical entity based on real-time data, (3) and human–computer interaction–based visual monitoring of authenticity and safety coordination. Through the case of the vibration test for the crowned blade of a steam turbine by blade tip-timing measurement, the feasibility and flexibility of DTTPM are demonstrated. [ABSTRACT FROM AUTHOR]

In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches. [ABSTRACT FROM AUTHOR]

At automotive manufacturing sites, meeting the delivery schedule is difficult owing to the occurrence of unpredictable abnormal scenarios such as product defects and equipment failures. To overcome this, manufacturing technologies developed as part of the Fourth Industrial Revolution are employed to meet the delivery schedule set by the customer. We propose a digital twin (DT)–based cyber-physical system (CPS) that can predict whether a product can be manufactured as per the schedule requested by a customer at an automotive body production line where abnormal scenarios occur. We designed a product, process, plan, plant, and resource information model for automotive body production lines; the proposed DT employs this model. Unlike in previous research on DTs focusing on independent engineering application development, we designed and implemented a CPS combined with a DT and other components for a Web-based integrated manufacturing platform. To the best of our knowledge, this is the first time a DT-based CPS is implemented for abnormal scenarios involving automotive body production lines; the capability of the proposed system was verified via experiments. The experimental results indicate that the proposed system achieved an average prediction performance of 96.83% for the actual production plan. We confirmed that the DT-based CPS can be applied to automotive body production lines, and it provides an advanced solution to predict whether production is possible according to the production plan. [ABSTRACT FROM AUTHOR]

The successful realization of Industry 4.0 depends much on how coherently the cyber and physical realms are conjoined in cyber-physical systems. In the context of the fourth industrial revolution, research efforts have mostly been channeled toward the cyber domain, whereas the physical domain has received significantly lesser consideration. A physical domain generally comprises material shaping equipment, work material, tools, working medium, sensors, automation technology, and connectivity mechanisms. The article provides a comprehensive review of the published literature to establish the states of readiness of the two most important manufacturing technologies: subtractive and additive and their sustainable merger from the perspective of Industry 4.0. Rich potentials in the four characteristics at the process level: speed, sustainability, agility, and customer centricity and three at the system level: connectivity, data collection, and automation are required for a manufacturing system (physical domain) to be Industry 4.0 compatible. The review establishes that the subtractive manufacturing domain is nearly compatible regarding speed and agility but needs improvements in respect of sustainability and customer centricity. Additive manufacturing, on the other hand, appears strong on agility and customer-centricity fronts but needs amelioration regarding production speed and sustainability. In respect of the system level characteristics, both technologies seem to be compatible regarding automation, whereas significant improvements are required in connectivity and data sensing and collection. For the sake of raising compatibility levels of the manufacturing systems, subtractive-additive amalgamation is scrutinized. The amalgamation, especially in a done-in-one configuration, has, reportedly, succeeded to retain the favorable traits of the two manufacturing technologies, thus, bringing the merger much closer to the Industry 4.0 requirements. Proper process planning and optimal work distribution between the subtractive and additive modes are critical for operating an amalgamated system at high levels of the key characteristics. [ABSTRACT FROM AUTHOR]

Cyber Physical Systems (CPSs) play a crucial role in the Industry 4.0 paradigm. The application of CPSs in production and manufacturing environments gave rise to the term Cyber Physical Production Systems (CPPSs). There is a growing interest in CPPSs, yet research in this area is scattered and needs to be reviewed for understanding their development status and maturity. The aim of this study is to carry out a systematic literature review (SLR) to analyze the current research activities on CPPSs according to their contributions to the engineering life cycle of such production system. Firstly, a method for SLR is presented. Then, literature analysis of CPPSs is conducted to present research activities in the light of the concept development and engineering development stages. Finally, based on the results of the literature analysis, a concept map of CPPSs research is proposed, which depicts the existing research topics in the engineering life cycle of CPPSs. And we exploit it to propose a research agenda of the CPPSs integration process required to ensure their efficient industrial use. Findings of this review can help researchers to examine the maturity of the development status of CPPSs, to discover which phases require improvement, and to know the future research directions for their industrial practices. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 (I4.0), which uses smart manufacturing as a strategy to solve industry challenges, is associated with significant research efforts that focus on cyber-physical systems, the internet of things, big data, and artificial intelligence. Utilizing I4.0 system performance and decision-making technologies, real-time monitoring, and system control, we developed a smart welding station system. The platform provides guidance for implementing a reliable welding system with a minimal amount of effort using certain characteristics of I4.0 technologies. In this study, a practical guide for the application of Reference Architectural Model Industry 4.0 (RAMI 4.0) and the use of technologies for smartization of welding systems are presented. First, we discuss the conceptual approach of a smart welding station system to create a platform for the station. Enabling technologies and their working functions are then defined. In addition, characteristics of RAMI 4.0 that apply to implement the system into the major architecture of selected RAMI 4.0 areas are analyzed. Finally, a designed platform for a smart welding station system based on five layers and three hierarchies of the production of RAMI 4.0 is presented. [ABSTRACT FROM AUTHOR]

Current concept, development, and testing applications in production concerning Cyber-Physical Systems (CPS), Industry 4.0 (I40), and Internet of Things (IoT) are mainly addressing fully autonomous systems, fostered by an increase in available technologies regarding distributed decision-making, sensors, and actuators for robotics systems. This is applied also to production logistics settings with a multitude of transport tasks, e.g., between warehousing or material supply stations and production locations within larger production sites as for example in the automotive industry. In most cases, mixed environments where automated systems and humans collaborate (e.g., cobots) are not in the center of analysis and development endeavors although the worker's adoption and acceptance of new technologies are of crucial relevance. From an interdisciplinary research perspective, this constitutes an important research gap, as the future challenges for successful automated systems will rely mainly on human-computer interaction (HCI) in connection with an efficient collaboration between motivated workers, automated robotics, and transportation systems. We develop a HCI efficiency description in production logistics based on an interdisciplinary analysis consisting of three interdependent parts: (i) a production logistics literature review and process study, (ii) a computer science literature review and simulation study for an existing autonomous traffic control algorithm applicable to production logistics settings with the specific inclusion of human actors, and (iii) a work science analysis for automation settings referring to theoretical foundations and empirical findings regarding the management of workers in digital work settings. We conclude with practical implications and discuss avenues for future research and business applications. [ABSTRACT FROM AUTHOR]

A fourth industrial revolution, occurring in global manufacturing, provides a vision of future manufacturing systems that incorporate highly dynamic physical systems, robust and responsive communications systems, and computing paradigms to maximize efficiency, enable mobility, and realize the promises of the digital factory. Wireless technology is a key enabler of that vision. A comprehensive graphical model is developed for a generic wireless factory work cell which employs the Systems Modeling Language (SysML), a standardized and semantically rich modeling language, to link the physical and network domains in such a cyber-physical system (CPS). The proposed model identifies the structural primitives, interfaces, and behaviors of the highly connected factory work cell in which wireless technology is used for significant data flows involved in control algorithms. The model includes the parametric definitions to encapsulate information loss, delay, and mutation associated with the wireless network, and it identifies pertinent wireless information flows. [ABSTRACT FROM AUTHOR]

Industrial revolutions have transformed manufacturing to mass production. However, there is a controversy about what production paradigm is emerging along with the incoming new industrial revolution. Most focus is either on smart manufacturing or 3D printing (additive manufacturing). This study converges smart manufacturing and additive manufacturing to form the long tail of making things as a paradigm shift for the new industrial revolution, which results in an ideal production system with the advantages of both economies of scale and scope. With the introduction of social aspects, socio-cyber-physical system (SCPS)-based manufacturing is proposed from the organizational semiotic perspective and exemplified by the four pillars of Future Internet. Furthermore, an extended version of the long tail is formed by the two modes of SCPS-based manufacturing: wisdom manufacturing and social manufacturing, which reviewed as the logical extension of smart manufacturing and additive manufacturing, respectively. Finally, a general interoperability model for SCPS-based manufacturing is suggested, which can also be applied to other integrated manufacturing systems. [ABSTRACT FROM AUTHOR]