Concept of Socio-Cyber-Physical Work Systems for Industry 4.0

In this paper the concepts of advanced production systems based on the challenges that bring the new industrial revolution named - Industry 4.0 are presented. The presented concept of socio-cyber-physical work systems is based on connecting social, cyber and physical working environments into a single functional, productive entity of the appointed elementary socio-cyber-physical work system.The elementary socio-cyber-physical work system is a basic building block of the cyber-physical production systems at the manufacturing level. The cyber system of the elementary socio-cyber-physical work system enables autonomous decision-making and cooperation in the network system. The possibility of implementing the proposed concept is based on the introduction of agency technologies in the domain of modern production systems and the development of information-communication technologies for the advanced management and control of cyber-physical production systems. Some illustrative examples reflect the experimental results of a research work in the field of cyber-physical systems and demonstrate the potential possibilities of implementing the concept of socio-cyber-physical work systems in the real industrial environment

Cyber-physical systems (CPS) in supply chain management: from foundations to practical implementation

Abstract Since 2015 developments such as Industry 4.0 and cyber-physical production systems on the technology side, and approaches such as flexible and smart manufacturing systems hold great potential. These in turn give rise to special requirements that the production planning, control and monitoring, among others, needing a paradigm shift to exploit the full potential of these methods and techniques. Starting from foundations in Cyber Physical Systems (CPS), building upon definitions and findings reported by literature, a practical example of innovative Cyber Physical Supply Chain Planning System (CPS2) is provided. The paper clarifies the advantages of cyber-physical systems in the production planning, controlling and monitoring perspective with respect to manufacturing, logistics and related planning practices. A set of basic features of CPS2 systems are discussed and addressed by contextualizing service orientation architecture and microservices components with respect to supply chain management collaboration and cooperation practices. The identification of specific technologies behind those functions, within the developed research, provides some practical insight if the interesting CPS2 potential

Cyber-physical systems (CPS) in supply chain management: From foundations to practical implementation

Since 2015 developments such as Industry 4.0 and cyber-physical production systems on the technology side, and approaches such as flexible and smart manufacturing systems hold great potential. These in turn give rise to special requirements that the production planning, control and monitoring, among others, needing a paradigm shift to exploit the full potential of these methods and techniques. Starting from foundations in Cyber Physical Systems (CPS), building upon definitions and findings reported by literature, a practical example of innovative Cyber Physical Supply Chain Planning System (CPS2) is provided. The paper clarifies the advantages of cyber-physical systems in the production planning, controlling and monitoring perspective with respect to manufacturing, logistics and related planning practices. A set of basic features of CPS2 systems are discussed and addressed by contextualizing service orientation architecture and microservices components with respect to supply chain management collaboration and cooperation practices. The identification of specific technologies behind those functions, within the developed research, provides some practical insight if the interesting CPS2 potential

Determinants of an Appropriate Degree of Autonomy in a Cyber-physical Production System

AbstractClassical productions systems are migrating step-by-step into cyber-physical production systems. The addition of much more computing power and object-bound data storage will lead to new possibilities for the advancement of autonomy in production systems. Autonomous message exchange and coordination can help to prevent quality problems (for instance wrong pairing of tool and work piece) and improve the disturbance management (for instance by faster information about current and probable disturbances). Due to the fact that nearly all improvements of existing production systems with cyber-physical systems take place in real and active manufacturing sites, on-site experiments for determining an appropriate degree of autonomy for production objects are not feasible. Therefore, a lab approach is necessary. In this contribution a hybrid lab approach to simulate various degrees of autonomy is presented [1]. The paper starts with a definition of autonomy and suggests diverse measurement methods [2]. After a short introduction into the lab concept, the results of some test runs are presented where autonomous objects perform the same production program as “dumb” production objects. Finally, an outlook for further research is given

Knowledge Graphs for Data And Knowledge Management in Cyber-Physical Production Systems

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen

Enabling the human in the loop: Linked data and knowledge in industrial cyber-physical systems

Industrial Cyber-Physical Systems have benefitted substantially from the introduction of a range of technology enablers. These include web-based and semantic computing, ubiquitous sensing, internet of things (IoT) with multi-connectivity, advanced computing architectures and digital platforms, coupled with edge or cloud side data management and analytics, and have contributed to shaping up enhanced or new data value chains in manufacturing. While parts of such data flows are increasingly automated, there is now a greater demand for more effectively integrating, rather than eliminating, human cognitive capabilities in the loop of production related processes. Human integration in Cyber-Physical environments can already be digitally supported in various ways. However, incorporating human skills and tangible knowledge requires approaches and technological solutions that facilitate the engagement of personnel within technical systems in ways that take advantage or amplify their cognitive capabilities to achieve more effective sociotechnical systems. After analysing related research, this paper introduces a novel viewpoint for enabling human in the loop engagement linked to cognitive capabilities and highlighting the role of context information management in industrial systems. Furthermore, it presents examples of technology enablers for placing the human in the loop at selected application cases relevant to production environments. Such placement benefits from the joint management of linked maintenance data and knowledge, expands the power of machine learning for asset awareness with embedded event detection, and facilitates IoT-driven analytics for product lifecycle management

Network Resource Management For Cyber-Physical Production Systems Based On Quality of Experience

In today's industrial challenges, it can be observed that the trends point in the direction of agile, wireless connected robots where elements of intelligence and control are implemented in the edge cloud. This paper outlines the roles of three key participants in the value chain of an industrial process: the network provider, the robot operator, and the customer. It proposes a scheme where the Quality of Service (QoS) parameters of the robot are fed into the network to inform network resource management. A sanding process use case is simulated to demonstrate the relationship between QoS and Quality of Experience (QoE) for each participant, quantitatively