Exploring Docker Containers for Time-sensitive Applications in Networked Control Systems

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.

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

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

Middleware-based Cyber-Physical Production System Modeling for Operators

One of the most significant directions in the development of computer science and information and communication technologies is represented by Cyber-Physical Systems (CPS) which are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the internet. Cyber-Physical Production Systems (CPPS), relying on the newest and foreseeable further developments of computer science, information and communication technologies on the one hand, and of manufacturing science and technology, on the other, may lead to the 4th Industrial Revolution, frequently noted as Industrie 4.0. The paper underlines that there are significant roots generally – and particularly in the CIRP community – which point towards CPPS. Expectations and the related new R&D challenges are also outlined.

Recent developments in the field of cyber physical systems (CPS) and internet of things (IoT) open up new possibilities in manufacturing. CPS and IoT represent enabling means for facilitating the companies’ adaptation to the ever-changing market needs and adoption of individualized manufacturing paradigm. It is foreseen that implementation of CPS and IoT will lead to new industrial revolution known as Industry 4.0. The fourth industrial revolution will bring about the radical changes in manufacturing process control through distribution of control tasks to intelligent devices. On the other hand, over the last decades International Electrotechnical Commission invested significant efforts in generation of the standard IEC 61499 for distributed automation systems. In this paper we provide the outline of the interconnection of IEC 61499 standard and Reference Architecture Model Industrie 4.0 in cyber physical production systems. The findings of the paper are illustrated using a realistic case study—an example of pneumatic manipulator that is made of CPS devices.

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.

A new generation of industrial revolution represented by intelligent manufacturing has come. Multi-product and small batch have become trend in market demands nowadays. Cyber-physical production system is a useful tool to meet this trend by performing tasks intelligently. Since cyber-physical production system performs tasks intelligently and autonomously, tasks and resources should be defined in standardized form in order to be identified and analyzed. Thus, how to define and analyze tasks and resources become the prerequisite for the operation of cyber-physical production system. In this article, a tasks and resources matching and analysis method is proposed for cyber-physical production system. First, the form of process knowledge base is designed, and the method for tasks and resources definition and matching is discussed. Next, the model of system operation logical based on colored Petri net and processing route generation algorithm is built. The parameters forecast method for non-standard processes is designed based on backpropagation neural network. Finally, implementation mechanism and effectiveness of the method are verified by a prototype system.

Cyber-physical systems, or Cyber-physical production systems in the manufacturing domain, are considered a fundamental technological enabler on the industry 4.0 revolution. In fact, these are interconnected physical and virtual components that provide real time monitoring on the manufacturing execution, for an intelligent management and advanced decision-making to ensure efficiency and reactivity improvement. Traditionally, on manufacturing operation, performance management systems have managed production by piloting the operations towards the expected objectives. However, this approach is limited on cyber physical systems due to the following difficulties: a) synchronous and asynchronous behavior on a real time context causing an unsuitable periodic calculation of performance indicators; and b) human limitations for real-time decision-making due to the complexity of problem and need of prompt analysis and action during execution. The main objective of this paper is to characterize five possible localizations of a potential performance management mechanism on the architectural arrangement of cyber-physical production systems, ranging from traditional centralized to distributed approaches. The purpose of this study is to assess the possible localizations of this mechanism thought the main performance steps including the objectives and results definition, the execution monitoring, the current state analysis, the performance reporting and the launching of the corrective actions.KeywordsCyber-physical systemsIndustrial performancePerformance management systemsIndustry 4.0

Industry 4.0, IoT, and cyber-physical production systems (CPPSs) are regarded as a key factor for manufacturing companies to become more efficient, flexible, and responsive to market changes. However, all but real-time bidirectional communication between shop floor cyber-physical systems (CPSs) and IoT-based production planning and control (PPC) in a cloud environment become especially challenging regarding the de facto state of technology, as we indicate. Many CPS/CPPS projects fail in industrial practice. We describe the design and implementation of a highly automated CPPS in a real-time make-to-order usage case for the production of prescription lenses based on an environment that requires real-time communication if it is to work. We use the project experiences to explain the lessons learned and derive a holistic approach to IoT-PPC CPS and CPPS design and implementation.

Anomaly detection is essential for realising state-of-the-art and safe cyber-physical production systems. With the early detection of possible anomalies, the rise and the carryover of a failure throughout the entire manufacture can be prevented. Current anomaly detection approaches use sensor data from the live system only. Therefore, many important characteristics, interactions, and correlations within the system are neglected. Graphs open a particular way of modelling cyber-physical production systems, while at the same time, they allow the inclusion of a wider variation and variety of data about the system. In this study, we introduce an unsupervised correlation- and interaction-aware anomaly detection concept for cyber-physical production systems based on graph neural networks. We define a concept that allows modelling the entire system as a graph, including external knowledge. We introduce a reconstruction-based graph neural network to detect and analyse the anomalies in these graph structures. The concept is evaluated in a real industrial cyber-physical production system. The test results confirm that the presented concept can be applied to detect anomalies while concurrently considering the correlations and interactions of the system.

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.

Industry 4.0 denotes a recent trend that aims at exploiting Cyber Physical Systems (CPS), based on IoT (Internet of Things) and cloud computing technologies, to obtain increased degrees of cooperation and communication in production systems, thus leading to what is referred to as Cyber Physical Production Systems (CPPS) or “Smart Factories”.