Integrated Component Data Model Based on UML for Smart Components Lifecycle Management: A Conceptual Approach

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.

A method for supporting the transformation of an existing production system with its integrated Enterprise Information Systems (EISs) into a Cyber Physical Production System (CPPS)

A problem-solving ontology for human-centered cyber physical production systems

Industry 4.0, a new industry initiative in Germany, is impacting strongly both on industry and on society. Many newspapers and technical magazines are publishing the state of the art articles on topics such as smart manufacturing based on IoT (Internet of Things), CPPS (Cyber Physical Production System), and cloud-based systems. Other parts of the world have started initiatives such as the IIC (Industrial Internet Consortium) in the US and the IVI (Industrial Value Chain Initiative) in Japan. Smart manufacturing is the key concept underlying these new initiatives. This special issue addresses the most advanced research on smart manufacturing. Subjects cover cyber-physical product-service systems, machinery production lines, manufacturing system simulation, lot-size energy-consumption dependence per production throughput unit, additive manufacturing processes, sensor network technology, production management technology, supply chain management technology, and smart manufacturing reviews. We thank the authors for their careful work and the reviewers for their incisive efforts without which this special issue would not have been possible. We hope that this special issue will trigger further research on smart manufacturing and its advances.

Rapid technology advances and increasing digitalization bring new opportunities but also put new challenges to modern production systems. Industrial agent concepts and technologies can be used to address the needs of modern Cyber-Physical Production Systems (CPPS). To do so, their interplay, especially in industrial settings, needs to be better understood and assessed. Key directions for investigations that are relevant for industrial agent based CPPS are analyzed in this work, mainly the design patterns, interfaces between agents and CPPS, metrics to evaluate the quality of agent based CPPS, and distributed intelligence implemented in or by agents. Although efforts exist both in agent and CPPS domains, their amalgamation still has several aspects that are under-investigated, especially when considering large-scale systems of CPPS, and the proposed four directions discussed in this work, seem promising to address several of the key underlying challenges.

In the context of Industry 4.0, Cyber-Physical Production Systems (CPPS) and digital twins are key technologies for the management of huge amount of data generated by Industrial Internet of things (IIoT) devices. However, the interoperability and flexibility of different components is still an important challenge so as to integrate them in the process and fit all industrial specific needs. Thus, the main contribution of this paper is to propose a database architecture and a data model associated allowing multiple agents to work collaboratively and synchronously to perform high-level tasks. Therefore, it fulfils requirements and needs of Industry 4.0: interoperability, scalability, flexibility and resilience. The proposed architecture and model are implemented on a cyber-physical production system (CPPS) which is used in order to show and discuss several use cases examples.

Volatility and uncertainty of today's value chains along with the market's demands for low-batch customized products mandate production systems to become smarter and more resilient, dynamically and even autonomously adapting to both external and internal disturbances. Such resilient behavior can be partially enabled by highly interconnected Cyber-Physical Production Systems (CPPS) incorporating advanced Artificial Intelligence (AI) technologies. Multi-agent solutions can provide better planning and control, improving flexibility and responsiveness in production systems. Small modular parts can autonomously take intelligent decisions and react to local events. The main goal of decentralization and interconnectivity is to enable autonomous and cooperative decision-making. Nevertheless, a more efficient orchestration of various AI components and deeper human integration are required. In addition, global behaviors of coalitions of autonomous agents are not easily comprehensible by workers. Furthermore, it is challenging to implement an Industry 4.0 paradigm where a human should be in charge of decision-making and execution. This paper discusses a Multi-Agent System (MAS) where several software agents cooperate with smart workers to enable a dynamic and reconfigurable production paradigm. Asset Administration Shell (AAS) submodels hold smart workers' descriptions in machine-readable format, serving as an integration layer between various system's components. The self-description capability of the AAS supports the system's adaptability and self-configuration. The proposed concept supports the plug-and-produce functionality of the production modules and improves human-machine integration in the shared assembly tasks.

Cyber-physical production systems are highly flexible systems that enable adaptive production processes. In these systems, all participants of the production process possess individual information about themselves and are equipped with sensors, actors, and communication interfaces. They can interact with each other and autonomously develop and execute process relevant decisions. For each component, a standard process sequence and alternative process sequences are defined. If a deviation in the standard process occurs during the production of an individual component, the process participants can interact with each other and autonomously define an appropriate response strategy and execute it by using actors of the participants and the intralogistics. Regardless of the deviation, the manufacturing and assembly process of individual components in cyber-physical production systems can still proceed. For this purpose, process deviations and the response behavior of cyber-physical production systems are analyzed, modeled, and simulated, to illustrate the benefits of cyber-physical production systems and to develop a process deviation management system for actual, physical production systems based on cyber-physical systems.

Various studies have been conducted on cyber-physical production systems (CPPS), a core technology for the implementation of smart manufacturing. However, existing studies are mostly conceptual or at an early stage, such as the proposal of a reference architecture. To achieve the practical implementation of CPPS, a systematic methodology for the collection, processing, and application of the data for CPPS is required. This is because CPPS can be successfully implemented only when processing criteria and application methods for the diverse data that change in real time because of the nature of a manufacturing shop floor are presented. Various technologies and systems have been developed for collecting raw data from a shop floor, but they are mainly focused on the automation of manufacturing. Thus, more detailed and systematic research is required for more efficient application of such technologies using a cyber model, which is the core of CPPS. For this purpose, in this article, a logic-based systematic methodology that can generate a throughput analysis model from the real-time data of a shop floor in a CPPS environment was proposed. Furthermore, logics that perform the Mapping, Scaling, and Calibration of the data of the shop floor into the machine, process, and factory levels were developed and their application to throughput analysis was described through a case study. The results of this study are expected to facilitate the practical implementation of CPPS and contribute to the successful implementation of smart manufacturing and the resultant revival of the manufacturing industry.

In order to overcome today´s challenges of increasing customer requirements, new methods for efficient and customized manufacturing processes have to be developed. This paper presents an approach for the reconfiguration of cyber-physical production systems (CPPS). The concept is based on state-of-theart virtual plant representations and mapping approaches. First, a suitable virtual plant representation has to be created and overall CPPS capabilities have to be determined. Then the CPPS capabilities need to be mapped with production requirements in order to identify CPPS adaptation needs. In case of necessary reconfigurations, the concept also provides a selection guide in terms of suitable best practices. The concept is applied to a human-robot interactive assembly process. The validation results prove the functionality and high practical relevance of the CPPS reconfiguration assistance.

The aerospace manufacturing industry exhibits significant complexity in all its tasks. For instance, the aircraft’s main body comprises several components with multiple dimensions, geometries, and materials. This kind of manufacturing system is specialised in creating aerospace parts characterised by advanced technology, limited production quantities, and a high degree of customisation. The aerospace products and the corresponding manufacturing systems have extensive life cycles spanning decades and are repurposed to accommodate product variations. Any disturbance during the project progression has the potential to result in escalated expenses and time investments, leaving economic and environmental drivers. In this way, Cyber-Physical Production Systems (CPPS) are emerging to reduce misinterpretation and mistakes across all stages of the manufacturing process. Therefore, the main aim of this paper is to discuss the current issues and emergent technologies across the literature review to address the following Research Issue 1 (RI1): What are the current issues and emergent technologies in CPPS and KDD for the Aerospace Industry? Research Issue 2 (RI2): What is the gap in CPPS and KDD for the aerospace industry? This initial literature review is concentrated on the Knowledge data-driven (KDD) to aid in developing CPPS for Aerospace Sheet Metal (ASM) parts manufacturing and examining the use of CPPS in the aerospace sector. Finally, this research contributes to the research community with an initial overview of research trends in the domain of KDD for CPPS in the aerospace industry and finds the main research gaps in this area.

V-model based development of cyber-physical systems and cyber-physical production systems

The potential benefits of the adoption of cyber-physical production systems (CPPSs) and their significant role in enabling smart manufacturing is well recognized today. However, it is less clear how such CPPS can be most effectively and consistently engineered and maintained throughout their lifecycle due to the existing divide in the information technology (IT) and operational technology (OT) landscape and ad hoc integration practices that result in inconsistent data and data models at various levels of manufacturing processes. The work presented in this article addresses this problem by envisioning a connective framework to support the engineering of CPPS through the use of a set of digital twins consistent with the real system throughout its lifecycle, not just used in the design and deployment phases. A review of the latest perspectives on using digital integration frameworks, methods, and solutions for lifecycle engineering of CPPS is provided in this article. This article demonstrates how a suitable framework, named SIMPLE, can be realized to effectively address the lack of consistent data models throughout the engineering lifecycle, including implementation details and example cases developed by the authors at the Warwick Manufacturing Group (WMG) in selected industrial sectors. Consideration is given to supporting cyber-to-physical systems' connectivity and extendable engineering toolsets, forming the basis for multidisciplinary digital engineering environments. Key discussion points include the role and importance of effective integration of IT and OT, suitable frameworks for integration and collaboration.

Traditional manufacturing and production systems are in the throes of a digital transformation. By blending the real and virtual production worlds, it is now possible to connect all parts of the production process: devices, products, processes, systems and people, in an informational ecosystem. This paper examines the underpinning issues that characterise the challenges for transforming traditional manufacturing to a Cyber Physical Production System. Such a transformation constitutes a major endeavour for requirements engineers who need to identify, specify and analyse the effects that a multitude of assets need to be transformed towards a network of collaborating devices, information sources, and human actors. The paper reports on the e-CORE approach which is a systematic, analytical and traceable approach to Requirements Engineering and demonstrates its utility using an industrial-size application. It also considers the effect of Cyber Physical Production Systems on future approaches to requirements in dealing with the dynamic nature of such systems.

In concert with advances in information and communication technology and their application to manufacturing environments, physical entities in factories are acquiring more intelligence via integration with cyber systems. This integration brings about Cyber-Physical Production Systems and leads to smart manufacturing, the next generation manufacturing paradigm. In the new paradigm, high levels of agility, flexibility, and real-time control make it possible to keep the system running efficiently and self-organized. At the same time, however, it becomes difficult in a self-organized and decentralized system to capture the system's status, evaluate the system's performance, and predict the system's future events. In this article, we suggest improvements to smart manufacturing systems where the intelligence from smart entities could be fully utilized without losing system control. To achieve this goal, a solution for integrating schedule-driven production (push systems) and event-driven production (pull systems) is proposed to optimize both material flow and information flow for manufacturing operations. For each entity in a smart manufacturing system, details of decision making are encapsulated and its status is exposed. The status-based decisions filter out unimportant information and make smart manufacturing systems loosely-coupled and predictable. A simulation case study based on Devices Profile for Web Services [1] is used to illustrate the effectiveness of such an approach. The case study suggests that status-based decisions could be applied to smart manufacturing and that they can be part of an approach that balances the self-organized control with overall performance. Therefore, we can make full use of intelligent entities in lower levels of a factory while keeping the entire system under control.