Industry 4.0: A Survey on Technologies, Applications and Open Research Issues

Reference architecture for holonic manufacturing systems: PROSA

Digital Twin in manufacturing: A categorical literature review and classification

One of the most significant advances in the development of computer science, information and communication technologies is represented by the cyber-physical systems (CPS). They 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 latest, and the foreseeable further developments of computer science, information and communication technologies on 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 in general – and in particular to the CIRP community – which point towards CPPS. Expectations towards research in and implementation of CPS and CPPS are outlined and some case studies are introduced. Related new R&D challenges are highlighted.

Cyber-physical systems in manufacturing

Classical Sets and Fuzzy Sets.- Classical and Fuzzy Relations.- Membership Functions.- Defuzzification.- Fuzzy Rule-Based System.- Fuzzy Decision Making.- Applications of Fuzzy Logic.- Fuzzy Logic Projects with Matlab.

Introduction to Fuzzy Logic using MATLAB

One of the main driving forces in the era of cyber-physical systems (CPSs) is the introduction of massive sensor networks (or nowadays various Internet of things solutions as well) into manufacturing processes, connected cars, precision agriculture, and so on. Therefore, large amounts of sensor data have to be ingested at the server side in order to generate and make the "twin digital model" or virtual factory of the existing physical processes for (among others) predictive simulation and scheduling purposes usable. In this paper, we focus on our ultimate goal, a novel software container-based approach with cloud agnostic orchestration facilities that enable the system operators in the industry to create and manage scalable, virtual IT platforms on-demand for these two typical major pillars of CPS: (1) server-side (i.e., back-end) framework for sensor networks and (2) configurable simulation tool for predicting the behavior of manufacturing systems. The paper discusses the scalability of the applied discrete-event simulation tool and the layered back-end framework starting from simple virtual machine-level to sophisticated multilevel autoscaling use case scenario. The presented achievements and evaluations leverage on (among others) the synergy of the existing EasySim simulator, our new CQueue software container manager, the continuously developed Octopus cloud orchestrator tool, and the latest version of the evolving MiCADO framework for integrating such tools into a unified platform.

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Orchestrated Platform for Cyber-Physical Systems

Key Performance Indicators (KPIs) are frequently used for measuring a production systems’ performance. The selection of KPIs should lead to a set being as small as possible but taking into account all relevant aspects of the system. This paper provides an analytical approach to determine the set of relevant KPIs for specific production lines, allowing for a transparent and complete performance measurement. An LP was formulated for the proposed KPI model and a significant reduction of the number of KPIs used could be realized. The analytical model was tested in a real industrial application.

Performance measurement in flow lines – Key to performance improvement

Manufacturing has moved from local towards global economy in the past decades enabling new paradigms to come into practice, such as distributed manufacturing which aims at increasing companies’ fle...

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Trust-based resource sharing mechanism in distributed manufacturing

The paper outlines two attempts to enhance the performance of agent-based manufacturing systems using adaptation/learning techniques. Some agent-level parameters are introduced, by which the system behavior can be tuned. The research was carried out with the help of a simulation framework for agent-based manufacturing architectures developed by the authors earlier. A short description of the framework, including the agents' structure, communication and cooperation techniques, is also given. The paper briefly introduces the main DAI approaches supporting the realization of distributed manufacturing structures, such as agent-based and holonic manufacturing systems. Some main results of this field are also enumerated.

Approaches to Increase the Performance of Agent-Based Production Systems

In the paper different architectures with partly self-developed simulation packages are described illustrating the benefits of combining simulation and machine learning (ML) techniques in manufacturing. From the artificial intelligence (AI) and ML side, artificial neural networks, heuristic search, simulated annealing, and agent-based techniques are put into action. The applicability of the proposed solutions is illustrated by the results of experimental runs.

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Botond Kádár

Papers

Orchestrated Platform for Cyber-Physical Systems

Performance measurement in flow lines – Key to performance improvement

Trust-based resource sharing mechanism in distributed manufacturing

Approaches to Increase the Performance of Agent-Based Production Systems

Combined use of simulation and ai/machine learning techniques in designing manufacturing processes and systems