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The continuous manufacturing of biologics offers significant advantages in terms of reducing manufacturing costs and increasing capacity, but it is not yet widely implemented by the industry due to major challenges in the automation, scheduling, process monitoring, continued process verification, and real-time control of multiple interconnected processing steps, which must be tightly controlled to produce a safe and efficacious product. The process produces a large amount of data from different sensors, analytical instruments, and offline analyses, requiring organization, storage, and analyses for process monitoring and control without compromising accuracy. We present a case study of a cyber–physical production system (CPPS) for the continuous manufacturing of mAbs that provides an automation infrastructure for data collection and storage in a data historian, along with data management tools that enable real-time analysis of the ongoing process using multivariate algorithms. The CPPS also facilitates process control and provides support in handling deviations at the process level by allowing the continuous train to re-adjust itself via a series of interconnected surge tanks and by recommending corrective actions to the operator. Successful steady-state operation is demonstrated for 55 h with end-to-end process automation and data collection via a range of in-line and at-line sensors. Following this, a series of deviations in the downstream unit operations, including affinity capture chromatography, cation exchange chromatography, and ultrafiltration, are monitored and tracked using multivariate approaches and in-process controls. The system is in line with Industry 4.0 and smart manufacturing concepts and is the first end-to-end CPPS for the continuous manufacturing of mAbs. [ABSTRACT FROM AUTHOR]

This study synthesizes a model of the main engineering and production chains in the Smart factory of Industry 4.0. This study has three main stages: an overview of the main definitions of the concepts "engineering and production chain" and "model", the generation of working definitions of these concepts for the purposes of this article, and synthesis of a model of the main engineering and production chains in the Smart factory of Industry 4.0. It was concluded in the course of the analysis that the defined six main engineering and production chains in the Smart factory of Industry 4.0 are part of its cyber–physical production system. The tools that support the Model of the main engineering and production chains in the Smart factory of Industry 4.0 are also synthesized in this article. The essential added value for science and production engineering of the Model in the main engineering and production chains in the Smart factory of Industry 4.0 are defined for the first time in the literature as an option to deepen the processes of re-structuring the real traditional physical production into a cyber–physical production process by integrating specialized software products in the operation of each of the defined chains and the entire cyber–physical production system. [ABSTRACT FROM AUTHOR]

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The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber–physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line's local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems. [ABSTRACT FROM AUTHOR]

The subject of the study is the process of Information and Measuring System (IMS) designing as a component of the Cyber-Physical System (CPS) in the paradigm of Industry 4.0 (I4.0). The aim of the study is to develop methodological support for the design of IMS and Automatic Control Systems (ACS) as components of production CPS (CPPS), in particular, for Digital Factory. Objectives: to determine the conceptual model of IMS; choose the IoT model for structural synthesis, select the appropriate regulatory (sets of standards and implementation models) and hardware; perform R&D of IMS based on NB-IoT sensors; formalize the procedure for integrating components into the CPPS, develop the Asset Administration Shell. The methods used are: heuristic synthesis methods, experimental planning theory. The following results were obtained. The key role of optimally designed IMS level 4.0 in increased decision-making accuracy in CPPS management and control processes is demonstrated. The quality of control is improved both by quickly obtaining accurate information for updating models in cyber add-ons, and at the physical level, in ACS. The universal model of IMS implementation in CPPS was proposed. The stages of choosing the concept, structure, hardware and communication protocols of the IIoT ecosystem IMS + ACS have been performed. The methodology was tested during the development of the NB-IoT Tech remote monitoring system, which has a decentralized structure for collection data on resources consumed. The integration of the ecosystem as a component of CPPS at the appropriate levels of the architectural model RAMI 4.0 has been performed. Regulatory support has been formed and the functional aspect of the Asset Administrative Shell for CPPS integration has been developed. Conclusions. Scientific novelty: it is proposed to design the IMS as a component of the AAS of the cyber-physical system, according to the implementation methodology of its subsystems at the corresponding levels of RAMI4.0 and the selected IoT model. The new approach, called "soft digitalization", combines the approaches of Industry 3.0 and 4.0, it is designed for the sustainable development of automated systems to the level of cyber-physical systems and is relevant for the recovery of the economy of Ukraine. Practical significance of the results: the IoT-Tech system based on Smart sensors has been developed and tested. This information and measurement system is non-volatile and adapted to measure any parameters in automated systems of various levels of digitization.

To remain competitive in a highly dynamic environment, manufacturing companies have to quickly react to disturbances or changing customer requirements. To enable manufacturing systems to cover these dynamics, the concept of reconfigurable manufacturing systems was introduced. From a technical point of view, this concept has been exploited for the past 20 years, revealing several different design solutions. However, industrial application is still an exception. Our analysis led to the assumption that this is due to a lack of operator support for reconfiguration management. In addition, mostly individual aspects of reconfiguration are considered instead of exploiting the entire reconfiguration space at the system and machine level. Therefore, in this paper, we present a digital twin framework for reconfiguration management considering reconfiguration as a holistic problem. We evaluate the framework by conducting a case study and challenging it by evaluating the completeness based on a systematic literature review, and analyze if it follows good practice based on 32 requirements for digital twin frameworks.

Currently, the manufacturing management board applies technologies in line with the concept of Industry 4.0. Cyber-physical production systems (CPSs) mean integrating computational processes with the corresponding physical ones, i.e., allowing work at the operational level and at the strategic level to run side by side. This paper proposes a framework to collect data and information from a production process, namely, the burnishing one, in order to monitor real-time deviations from the correct course of the process and thus reduce the number of defective products within the manufacturing process. The proposed new solutions consist of (i) the data and information of the production process, acquired from sensors, (ii) a predictive model, based on the Hellwig method for errors in the production process, relying on indications of a machine status, and (iii) an information layer system, integrating the process data acquired in real time with the model for predicting errors within the production process in an enterprise resource planning (ERP) system, that is, the business intelligence module. The possibilities of using the results of research in managerial practice are demonstrated through the application of an actual burnishing process. This new framework can be treated as a solution which will help managers to monitor the production flow and respond, in real time, to interruptions.

In order to use tools, e.g. in machining, efficiently until the end of their life, predictions for the remaining tool lifespan are essential. These can only be made based on comprehensive life cycle data of the individual tools originating on different machines. Current approaches usually pursue a machine-related monitoring of the process data; a tool-related evaluation is thus not possible. The concept of the Asset Administration Shell (AAS), defined by Plattform Industrie 4.0, allows not only storing information but also describing it semantically so that interpretation is possible without a priori knowledge. They are thus suitable for making systems interoperable and can be used in the scenario described to manage the lifecycle data of tools. The AAS of a tool can move from one host to another together with the described tool and collect or provide data at the machine. A central server can be used to manage tools that are in storage and are used on any machine without a corresponding management capability. When a tool is removed from the tool magazine, the AAS is transferred to a central server where the tool information can be retrieved and from which the administration shell can be requested the next time the tool is used. While an exchange format is defined for the AAS, the necessary communication mechanisms for exchange between the central server and machines or machine-related devices must be defined. A registry in which all AAS are listed allows applications, such as an overarching user interface or an AI-based forecasting algorithm, to find the administration shells of all tools and access the individual data. The complete use case has been implemented exemplarily utilizing a central server and Sinumerik edge devices close to the machine tool. [ABSTRACT FROM AUTHOR]

This study demonstrates the result of a systematic literature review using the preferred reporting items for systematic review and meta-analysis (PRISMA) method. The main objective is to find issues that still need research on heterogeneous communication, especially 5G cellular and localization networks for small and medium-sized enterprises (SMEs) in the context of Industry 4.0. The research query is formulated based on the research questions and implemented in three selected databases. Then, the query results are categorized into six sections. Consequently, open issues addressed by the assessed papers in five general categories are analyzed and discussed. [ABSTRACT FROM AUTHOR]

Rechargeable battery production should yield highly diversified batteries, overcoming performance degradation caused by the complexity of production processes, dynamic disturbances, and uncertainties. Resilience must be achieved to overcome these limitations while satisfying the core technical requirements. This study developed an architectural framework for a cyber-physical production system (CPPS) using a digital twin (DT) to achieve resilience. Activities for resilience, operational characteristics, and CPPS were analysed to determine the core requirements. This analysis presents a novel model of activities for resilience. Moreover, the DT-based CPPS architecture, service composition procedures, and the asset description for providing inputs to the elements in the CPPS were designed according to these requirements. The proposed architectural framework applies the asset administration shell principles for efficient interoperability. The service composition procedures are classified into the type and instance phases to ensure static and dynamic technical functionalities. Moreover, the asset description is suitable to indicate the required information elements of rechargeable battery production. The DT-based CPPS was applied in a rechargeable battery production for an industrial case study to verify and validate the proposed method. The average accuracy of the DT application was 95.24%, indicating that it can provide technical functions with high accuracy. As a result, these technical functions can be executed within a sufficient action time, and the high simulation accuracy prevents performance degradation during production. Additionally, the DT is suitable for event diagnosis and provides a dynamic response. Furthermore, the proposed method can eliminate the data, analysis, and decision latencies. [ABSTRACT FROM AUTHOR]

This study synthesizes a model of the main engineering and production chains in the Smart factory of Industry 4.0. This study has three main stages: an overview of the main definitions of the concepts “engineering and production chain” and “model”, the generation of working definitions of these concepts for the purposes of this article, and synthesis of a model of the main engineering and production chains in the Smart factory of Industry 4.0. It was concluded in the course of the analysis that the defined six main engineering and production chains in the Smart factory of Industry 4.0 are part of its cyber–physical production system. The tools that support the Model of the main engineering and production chains in the Smart factory of Industry 4.0 are also synthesized in this article. The essential added value for science and production engineering of the Model in the main engineering and production chains in the Smart factory of Industry 4.0 are defined for the first time in the literature as an option to deepen the processes of re-structuring the real traditional physical production into a cyber–physical production process by integrating specialized software products in the operation of each of the defined chains and the entire cyber–physical production system.

Due to constantly changing conditions, demand, and technologies, companies increasingly seek flexibility. Productivity results from automation, improved working conditions and the focus of people in production in interaction with machines. Unfortunately, the human factor is often not considered to increase flexibility and productivity with new concepts. This work aims to develop a hybrid assistance system that allows a dynamic configuration of cyber-physical production systems considering the current order situation and available resources utilizing simulation. The system also considers human factors in addition to economic factors, which contributes to the extended economic appraisal. [ABSTRACT FROM AUTHOR]

Cyber-physical production systems (CPPS) represent a relevant aspect related to industry 4.0 and the advances promoted by the digitization and use of artificial intelligence in the production environment in the search for the development of smart factories. This study aims to assess the maturity level of cyber-physical production system (CPPS) within manufacturing industries in the Amazon. The research uses a quali-quantitative approach to analyze the problem by conducting exploratory case studies (indepth case) and the research framework used aimed to evaluate and measure the CPPS within three manufacturing industries in the Amazon (n = 3) to measure their maturity. Findings reveal a positive relationship between the type of production system adopted by the company, the level of automation, and the maturity of the CPPS. The proposed methodology can assist other companies in the development of the technological strategy, supporting the digital transformation process in order to obtain competitive advantage. The study contributes by addressing the topic of cyber-physical production systems from the point of view of operations management and strategy.

Cyber–Physical Production Systems (CPPSs) as distributed Systems of Systems (SoS) are at the center of attention from different industries. CPPSs face different categories of errors. These errors will cause failures of the entire production chain. To handle this concern, production systems should be converted into fault-tolerant production systems. To present such systems, a fault tolerance approach was developed to help possible faults prediction and detection of faults causes in this study. Also, the increasing complexity and uncertainty of CPPS call for Digital Twin (DT)-based fault tolerance approach. The proposes approach uses an extraction module to extract the faults signatures efficiently. Based on all extracted faults, appropriate responses could be generated through reliable faults patterns prediction. This method is provided using Fault Tree Analyzer (FTA), Zero-suppressed Decision Diagram (ZDD), and Support Vector Machine-Adaptive Neuro-Fuzzy Inference System (SVM-ANFIS) structure. The results based on digital twin-based CPPS of the food production system as a use case show that the proposed approach can predict reliable faults signatures to prevent failures and make a much reliable production system. Also, this method can guarantee that CPPS is up and running with optimal levels at all times. • Presenting a new digital twin-based fault tolerance approach. • Presenting an extraction module as a knowledge-based module for presenting faults and signatures of them. • Presenting prediction module as a soft computing-based module for defining reliable variable faults signatures in CPPS. • Making higher production per hour because of low average time to handle predicted faults in CPPS through proposed approach. [ABSTRACT FROM AUTHOR]

Recent technological advancements and the evolution of industrial manufacturing paradigms have substantially increased the complexity of product-specific production systems. To reduce the time cost of modelling and verification and to enhance the degree of uniformity in the modelling process of system components, this article presents a componentised framework for domain modelling and performance analysis based on the concept of "multi-granularity and multi-view" for a production line of personalised and customised products, for plug-and-play manufacturing processes to involving a large number of model input parameters. The coloured Petri net tool is utilised as a simulation tool for mapping domain models to computational models for simulation and performance evaluation. This paper presents a method for setting the input parameters of a production system when using WIP, through-put and cycle time as metrics. The results of the performance analysis demonstrate the applicability of the proposed framework and provide direction for the production line's layout design and scheduling strategy. [ABSTRACT FROM AUTHOR]

Reconfiguration demand is increasing due to frequent requirement changes for manufacturing systems. Recent approaches aim at investigating feasible configuration alternatives from which they select the optimal one. This relies on processes whose behavior is not reliant on e.g. the production sequence. However, when machine learning is used, components' behavior depends on the process' specifics, requiring additional concepts to successfully conduct reconfiguration management. Therefore, we propose the enhancement of the comprehensive reconfiguration management with transfer learning. This provides the ability to assess the machine learning dependent behavior of the different CPPS configurations with reduced effort and further assists the recommissioning of the chosen one. A real cyber-physical production system from the discrete manufacturing domain is utilized to demonstrate the aforementioned proposal. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically review digital twin-based cyber-physical production systems in immersive 3D environments. The findings and analyses highlight that multi-source heterogeneous product lifecycle data, machining equipments and tools, and digital manufacturing technology can be harnessed in digital twin-driven applications. Throughout March 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “digital twin” + “cyber-physical production systems,” “immersive 3D environments,” “virtual modeling and simulation tools,” “spatial data visualization techniques,” and “remote sensing technologies.” As research published in 2022 was inspected, only 144 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 19 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR. [ABSTRACT FROM AUTHOR]

Despite the relevance of digital twin-based product development and manufacturing processes in virtual space, only limited research has been conducted on this topic. In this article, we cumulate previous research findings indicating that digital twin-based product development and manufacturing processes in virtual space require performance optimization and maintenance scheduling. We contribute to the literature on digital twin-based smart manufacturing technologies and tools by showing that sensor-based data acquisition and analysis are pivotal in diagnosis and simulation of digital twin-based product development. Throughout February 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “digital twin” + “product development,” “manufacturing processes,” “data visualization tools and techniques,” “cloud computing technologies,” and “cyber-physical production systems.” As we inspected research published in 2022, only 154 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 23, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, Distiller SR, and MMAT. [ABSTRACT FROM AUTHOR]

The functionality of existing cyber-physical production systems generally focuses on mapping technologic specifications derived from production requirements. Consequently, such systems base their conception on a structurally mechanistic paradigm. Insofar as these approaches have considered humans, their conception likewise is based on the structurally identical paradigm. Due to the fundamental reorientation towards explicitly human-centered approaches, the fact that essential aspects of the dimension "human" remain unconsidered by the previous paradigm becomes more and more apparent. To overcome such limitations, mapping the "social" dimension requires a structurally different approach. In this paper, an anthropocentric approach is developed based on possible conceptions of the human being, enabling a structural integration of the human being in an extended dimension. Through the model, extending concepts for better integration of the human being in the sense of human-centered approaches, as envisioned in the Industrie 5.0 conception, is possible. [ABSTRACT FROM AUTHOR]

Since the share of renewable energy continues to grow, the resulting fluctuations of energy supply in the electricity grid must be balanced. Electricity consumers can help achieving this by reacting to fluctuations in supply and adjusting their demand (demand response). As one of the largest electricity consumers, industry should embrace this opportunity by implementing energy flexibility measures for demand response. In an energy-flexible production, machines have multiple objectives: First they must meet the traditional production targets: high quality, low cost, and short production time. Additionally, they should react to electricity price signals or to external power change requests and adjust their electrical consumption, for example by interrupting an active process. Forecasts of future machine behavior including energy consumption and the state of machine components help to improve the effectiveness of a proposed energy-flexible controller that enables machines to achieve these objectives. In addition, the machine and the controller must communicate with each other and external sources. Therefore, we propose the implementation of a cyber-physical production system (CPPS) for energy-flexible operation of production machines. Our CPPS consists of a simulation model for forecasting, an automation data model for controlling production machines via OPC UA and of a software framework, which is based on co-simulation, to provide the environment for controlling and optimizing the production machine in an energy-flexible manner. We show that co-simulation can be used to achieve energy-flexible operation of production machines and avoid unsafe states of the system. We apply the framework to an aqueous component cleaning machine performing a batch process. The operation of the electric tank heating element and the start times of cleaning process steps are optimized. With this work we show the successful development of the CPPS and the corresponding software framework, which will be transferred to other machines and more complex controllers in the future. [ABSTRACT FROM AUTHOR]

The change in the concept of an automation pyramid into an automation cloud in a cyber-physical production system makes data communication no longer stratified but can be done directly between devices. Based on IEC 61499, which defines the function blocks for building such communications, communication protocols can be run on various devices. Several communication protocols that can fulfill these requirements are OPC-UA, FBDK / IP, and MQTT. The research was conducted by comparing the three communication protocols for latency parameters and their jitters. The test method used to compare latency parameters is the variance analysis test and the Tukey test. The jitter value of the protocols are compared to the standard deviation parameter. The test results showed that the MQTT communication protocol had a faster latency value, with a 95% confidence level. The standard deviation of the variation value for OPC-UA, FBDK / IP, and MQTT showed the jitter value of 0.72 seconds, 0.35 seconds, and 0.64 seconds. Comparing the three communication protocols' standard deviation values showed that the FBDK / IP communication protocol has significantly less jitter than the OPC-UA and MQTT communication protocols.

The central claim of the paper is that the development and control of Cyber-Physical Production Systems (CPPS) requires a systematic approach to handle and include explicit ethical considerations. Since the contribution of artificial intelligence (AI) technologies, and of agent-based models in particular, was instrumental in the evolution of CPPSs, approaches of ethical AI should be endorsed in CPPS development by design. The paper discusses recent advances for ethical AI and suggests a pathway from ethical norms towards standards. As it is argued, taking the responsible AI approach is promising when tackling the main ethic-related challenges of Cyber-Physical Production Systems. We expose a number of dilemmas to be resolved so that AI systems incorporated in CPPS cause no damages either in humans, equipment or in the environment and increase the trust in the users of current and future AI technologies. [ABSTRACT FROM AUTHOR]

Cyber-physical production systems (CPPS) represent a relevant aspect related to industry 4.0 and the advances promoted by the digitization and use of artificial intelligence in the production environment in the search for the development of smart factories. This study aims to assess the maturity level of cyber-physical production system (CPPS) within manufacturing industries in the Amazon. The research uses a quali-quantitative approach to analyze the problem by conducting exploratory case studies (in- depth case) and the research framework used aimed to evaluate and measure the CPPS within three manufacturing industries in the Amazon (n = 3) to measure their maturity. Findings reveal a positive relationship between the type of production system adopted by the company, the level of automation, and the maturity of the CPPS. The proposed methodology can assist other companies in the development of the technological strategy, supporting the digital transformation process in order to obtain competitive advantage. The study contributes by addressing the topic of cyber-physical production systems from the point of view of operations management and strategy. [ABSTRACT FROM AUTHOR]

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing. [ABSTRACT FROM AUTHOR]

Social intelligence in robotics appeared quite recently in the field of artificial intelligence (AI) and robotics. It is becoming increasingly evident that social and interaction skills are essentially required in any application where robots need to interact with humans. While the workspaces have transformed into fully shared spaces for performing collaborative tasks, human–robot collaboration (HRC) poses many challenges to the nature of interactions and social behavior among the collaborators. The complex dynamic environment coupled with uncertainty, anomaly, and threats raises questions about the safety and security of the cyber-physical production system (CPPS) in which HRC is involved. Interactions in the social sphere include both physical and psychological safety issues. In this work, we proposed a connective framework that can quickly respond to changing physical and psychological safety state of a CPPS. The first layer executes the production plan and monitors the changes through sensors. The second layer evaluates the situations in terms of their severity as anxiety by applying a quantification method that obtains support from a knowledge base. The third layer responds to the situations through the optimal allocation of resources. The fourth layer decides on the actions to mitigate the anxiety through the allocated resources suggested by the optimization layer. Experimental validation of the proposed method was performed on industrial case studies involving HRC. The results demonstrated that the proposed method improves the decision-making of a CPPS experiencing complex situations, ensures physical safety, and effectively enhances the productivity of the human–robot team by leveraging psychological comfort.

Digital transformation and artificial intelligence are creating an opportunity for innovation across all levels of industry and are transforming the world of work by enabling factories to embrace cutting edge Information Technologies (ITs) into their manufacturing processes. Manufacturing Execution Systems (MESs) are abandoning their traditional role of legacy executing middle-ware for embracing the much wider vision of functional interoperability enablers among autonomous, distributed, and collaborative Cyber-Physical Production System (CPPS). In this paper, we propose a basic methodology for universally modeling, digitalizing, and integrating services offered by a variety of isolated workcells into a single, standardized, and augmented production system. The result is a reliable, reconfigurable, and interoperable manufacturing architecture, which privileges Open Platform Communications Unified Architecture (OPC UA) and its rich possibilities for information modeling at a higher level of the common service interoperability, along with Message Queuing Telemetry Transport (MQTT) lightweight protocols at lower levels of data exchange. The proposed MES architecture has been demonstrated and validated in several use-cases at a research manufacturing laboratory of excellence for industrial testbeds. [ABSTRACT FROM AUTHOR]

Facility management is an evolving discipline that has received attention from both professionals and researchers in recent years. Modern facility management considers various interests related to material resources, and among others, social and environmental interests. An important opportunity for the improvement of this discipline derives from the introduction of Industry 4.0 technologies for the management of material resources. In this paper, we shall study the problem of industrial facility management, an area with important economic implications. Starting from a facility management model for the maintenance of industrial assets, we develop a general approach to maintenance based on the Internet of Things and Cyber-Physical Systems, which allows us to reason about the implementation of an effective Organisational Facility Management Unit. The objective is the continuous improvement of maintenance activities, from which also derives the improvement of the production process performance. [ABSTRACT FROM AUTHOR]

This study discusses a framework to support manufacturers in their maintenance activities via data-driven condition monitoring and advanced visualization techniques. Moreover, the framework proposes a user-friendly predictive maintenance approach by enabling the user to configure the analysis chains by himself. Apart from this approach, the framework is built on containerization technologies to enable the platform to be flexible, resilient, and scalable in a wide range of production environments. The proposed techniques have been tested in a white goods use case with the preliminary results being reported in this work. [ABSTRACT FROM AUTHOR]

Digital Shadows are data structures precisely tailored to support decision making in domain-specific real-time that promise tremendous potential to reduce time and cost in manufacturing. They are often engineered ad-hoc, for single specific applications, without considering their aggregation, combination, or reuse. This lack of foundations hampers a joint understanding of Digital Shadows that prevents joint research as well as collaboration and exchange of Digital Shadows across enterprise boundaries. Based on interdisciplinary research, we conceived a conceptual model of Digital Shadows that can guide their engineering, combination, and reuse. This not only supports researchers and practitioners in better understanding each other when discussing Digital Shadows but also eases the engineering of compatible and exchangeable Digital Shadows. [ABSTRACT FROM AUTHOR]

Through the evolving usage of cloud and edge computing technologies in cyber-physical production systems (CPPS), distributed databases and a variety of data sources can be found in the production environment. For integrating adaptable safety functions in CPPS, the traceability and overall availability of engineering data and current process data from different production modules must be guaranteed at any time. Therefore, a concept as well as an implementation approach for an ontology-based data management system for accessing distributed and various data sources in CPPS is presented and discussed based on adaptable safety functions, which are implemented in a show case scenario. [ABSTRACT FROM AUTHOR]

During the last years, several research activities and studies have presented the possibility to perform manufacturing control using distributed approaches. Although these new approaches aim to deliver more flexibility and adaptability to the shop floor, they are not being readily adopted and utilised by the manufacturers. One of the main challenges is the unpredictability of the proposed solutions and the uncertainty associated with these approaches. Hence, the proposed research aims to explore the utilisation of Digital Twins (DTs) to predict and understand the execution of these systems in runtime. The Fourth Industrial Revolution is leading to the emergence of new concepts amongst which DT stand out. Given their early stage, however, the already existing implementations are far from standardised, meaning that each practical case has to be analysed on its own and solutions are often created from scratch. Taking the aforementioned into account, the authors suggest an architecture that enables the integration between a previously designed and developed agent-based distributed control system and its DT, whose implementation is also provided in detail. Furthermore, the digital model's calibration is described jointly with the careful validation process carried out. Thanks to the latter, several conclusions and guidelines for future implementations were possible to derive as well. [ABSTRACT FROM AUTHOR]

In recent years, the research on Digital Twin is in the ascendant. As a new paradigm or concept, it shows great potential. However, the connotation and scope of the Digital Twin concept is still uncertain, especially the Digital Twin Model definition is not clear.According to the pattern category, the Digital Twin Model can be divided into general model and special model, in which the special model is still the focus of current research, and the research content is mainly embodied in the use of Digital Twin method to model specific projects. It also includes concept for developing specialized models. These specific projects in addition to the traditional manufacturing related to parts measurement and quality control, manufacturing, design and work processes, as well as system management, but also in the field of biomedical applications and applications for petroleum engineering and so on. There are many tools and techniques for developing special models, such as general industrial software, special industrial software, simulation platform and self-developed secondary development tools, etc.The research object of the Digital Twin general model is not specific to a specific project, but how to represent the controlled elements of the model as a group of common objects and the relationships between these objects. This provides a consistent approach to the management and communication of controlled elements between different environments. The research on the general model is mainly divided into the conceptual research and the model implementation method; the research heat of the two directions is almost the same. Conceptual research ranges from product lifecycle management to system behavior description, such as general system behavior and system reconfiguration, and to product configuration management, to specific workflow, such as design methods, manufacturing systems and manufacturing processes. The research content is relatively divergent, and there is no particularly prominent hot spot. The research of Digital Twin general model implementation is mainly reflected in the modeling language construction, the model development methods exploration, the specific tools usage, the Meta-model concept implantation and the model algorithm exploration.Digital Twin Model is one of the core areas of Digital Twin research. Its future research focuses on how to integrate the external features and intrinsic properties from different Digital Twin artifacts into a model with interoperability, interactivity and scalability for more efficiently realizing the information flow between the physical world and the digital world, thus achieving the universal Digital Twin application, and then supporting the CPS (Cyber Physical Space) and CPPS (Cyber Physical Production System) construction. To this end, the next problem in the Digital Twin Model needing to be solved first is how to dock the standard reference architecture, such as the RAMI4.0 (Reference Architecture Model Industrial 4.0) proposed by Germany and the IMSA (Intelligent Manufacturing System Architecture) by China, etc. Secondly, the Digital Twin Model needs a unified method to describe and it also needs consistent conclusions, in order to standardize the models established by independent development, thus improving the interoperability and scalability of the model. Otherwise, the performance of the model will decrease significantly as the system scales raise. Thirdly, the research on China's Digital Twin Model requires the support of domestic professional industrial software and modeling software, so that the Chinese scholars can carry out in-depth research that is more in line with national conditions.

Cyber-Physical Production Systems (CPPS) are inherent to i4.0 to enhance actual manufacturing system control and management processes, and, consequently, the decision making process. This paper presents research on the influence of decision making duration time in CPPS on manufacturing system performance, for different scheduling paradigms. Different scenarios were investigated considering different decision making duration times and different variations of the environment dynamics. Results show that with the increase of the decision making duration time, the completion time of the jobs given at the input, as one of the principal manufacturing system performance measures, increases. Also, considering stable environments, the completion time variation growth in function of CPPS decision making duration time is approximately linear, while when considering dynamic environments, completion time variation growth in the function of CPPS decision making duration time is nonlinear.

Machine vision (MV) can help in achieving real-time data analysis in a manufacturing environment. This can be implemented in any industry to achieve real-time monitoring of workpieces for geometric defects and material irregularities. Identification of defects, sorting of workpieces based on their physical parameters, and analysis of process abnormalities can be achieved by using the real-time data from simple and cost-effective raspberry pi with camera and open source machine learning platform TensorFlow to run convolutional neural network (CNN) model. The proposed cyber-physical production system enables to develop a MV based system for data acquisition integrating physical entities of learning factory (LF) with the cyber world. Nowadays, LFs are widely used to train the workforce for developing competencies for emerging technologies and challenges faced due to technological advancements in Industry 4.0. This paper demonstrates the application of a cost-effective MV system in a learning factory environment to achieve real-time data acquisition and energy efficiency. The proposed low-cost machine vision is found to detect geometric irregularities, colours and surface defects. The simple cost effective MV system has enhanced the energy efficiency and reduced the total carbon footprint by 18.37 % and 78.83 % depending upon the location of MV system along the flow. The teaching-learning experience is also enhanced through action-based learning strategies. This not only ensures less rework, better control, unbiased decisions, 100% quality assurance but also the need of workers/operators can be reduced. [ABSTRACT FROM AUTHOR]

The current introduction of Industry 4.0 is very challenging for industrial companies. On the one hand, there is an urge to implement concepts such as digital worker assistance systems or cyber-physical production systems, but besides theoretical work, there is very little research that shows examples of its practical implementation. Furthermore, there is currently a lack of a clear model of how sensor-based worker assistance systems for data acquisition and analytics can be designed and systematically implemented. In the present research, a model for a vision-based worker assistance system for assembly was developed based on an industrial case study regarding a manual assembly line. The proposed model consists of five integrated modules: data acquisition, data preprocessing, data storage, data analysis, and simulation. The data acquisition module was constructed in the assembly workstation of the production line by implementing a depth camera, which together with an algorithm developed in Python for preprocessing, tracks the activities of the operator and inserts the processing times into a SQL table of the data storage module. This module contains all the relevant information of the production system, from the shop floor to the Manufacturing Execution System, enabling vertical integration. The data analysis module, aimed at the streaming and predictive analytics, was deployed in the RStudio platform. Likewise, the simulation module was conceptualized to retrieve real-time data from the shop floor and to select the best strategy. To evaluate the model testing of the proposed system in real production was performed. The results of this use case provide useful information for academia as well as practitioners how to implement vision-based worker assistance systems. [ABSTRACT FROM AUTHOR]

The optimization of production systems is a core-business for manufacturing companies. Modern approaches focus on strategic optimization via the concept of the Digital Twin. In this paper we present a model for the operational optimization of manufacturing systems via the core technology of the Digital Shadow. The model combines three main-aspects: automation, universality and the utilization of real-time data. Automation is achieved by a self-developed algorithm, universality by the application of a genetic algorithm and the integration of real-time-data through the utilization of the Digital Shadow. We validate the model by means of experimental-design. [ABSTRACT FROM AUTHOR]

The increasing gap in shopfloor operators' skillset regarding advanced information and communication technologies along with workforce's diversity require a cognitive system bridging such technical gaps in order to address evolving production demands and satisfy the human need for self-fulfillment and self-actualization at work. This study discusses on a two-level collaborative filtering approach to improve the distribution of information content provided to an operator for completing a manufacturing activity while considering his or her feedback. A prototype implementation is evaluated in a case study related to the operator's job rotation on a shopfloor that involves multiple workstations and tasks. [ABSTRACT FROM AUTHOR]

This paper analyzes the outcomes of an exploratory review of the current research on digital-twin-based cyber-physical production systems. The data used for this study was obtained and replicated from previous research conducted by Deloitte, Gartner, Job Wizards/Konica Minolta, and PTC. I performed analyses and made estimates regarding benefits of digital twins in companies, digital twin business values, practical actions to advance digital twin strategies, and different digital twins that enterprises can use. Data collected from 4,300 respondents are tested against the research model by using structural equation modeling. [ABSTRACT FROM AUTHOR]

The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

The world is experiencing a new industrial revolution characterized by intelligent manufacturing. Cyber-physical production systems (CPPSs) have become a research focus due to their proposed use as a solution to the development of flexible and reactive systems. The application of current centralized scheduling methods is difficult because of the enhanced precision control mode of a CPPS. Therefore, this paper focuses on distributed optimal scheduling based on multi-agent systems. First, the goals and constraints of the system are set, a two-layer decision model and the required indicators are designed to ensure the overall optimization effect, and the roles and functions of different agents are then set. Second, the dynamic decision cycle and the multistage negotiation mechanism based on the contract net protocol are studied to ensure the quality of negotiation. A rescheduling algorithm is designed to guarantee adaptability in the case of disturbance in the system. Finally, the applicability and superiority of the strategies are demonstrated via experiments and case studies.

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing.

The design of numerical control systems for industrial machinery is a difficult task, especially when you create universal modular equipment with computer numerical control (CNC). This article presents a modular approach to the design of such systems. The modular control system under consideration is based on a multi-agent network, in which each entity (module) acts as an integral and indivisible part of the object as well as the enlarged structure. This approach allows one to combine the advantages of classical hierarchical control systems with the flexibility and reliability of decentralized multi-agent networks and also to carry out seamless integration of equipment built on the basis of this architecture into a cyber-physical production system (CPPS). The proposed architecture is implemented in the control system of a universal industrial platform. As an example, the apparatus for selective laser curing of a photopolymer to the surfaces of arbitrary shapes is represented. The general structure of the installation determined by the basic hardware and software modules and the network communication protocol are described.