Ensuring safe and consistent coengineering of cyber‐physical production systems: A case study

2020 ◽  
Author(s):  
Michael Tröls ◽  
Atif Mashkoor ◽  
Andreas Demuth ◽  
Alexander Egyed
Keyword(s):  
Production Systems ◽  
Cyber Physical Production Systems

scholarly journals QHAR: Q-Holonic-Based ARchitecture for Self-Configuration of Cyber–Physical Production Systems

2021 ◽  
Vol 11 (19) ◽  
pp. 9013
Author(s):  
Douha Macherki ◽  
Thierno M. L. Diallo ◽  
Jean-Yves Choley ◽  
Amir Guizani ◽  
Maher Barkallah ◽  
...  
Keyword(s):  
Decentralized Control ◽  
System Performance ◽  
Production Systems ◽  
Control Level ◽  
Centralized Control ◽  
Four Dimensions ◽  
Potential Capacity ◽  
Heterarchical Control ◽  
Cyber Physical Production Systems

Production systems must be able to adapt to increasingly frequent internal and external changes. Cyber-Physical Production Systems (CPPS), thanks to their potential capacity for self-reconfiguration, can cope with this need for adaptation. To implement the self-reconfiguration functionality in economical and safe conditions, CPPS must have appropriate tools and contextualized information. This information can be organized in the form of an architecture. In this paper, after the analysis of several holonic and nonholonic architectures, we propose a holonic architecture that allows for reliable and efficient reconfiguration. We call this architecture QHAR (Q-Holonic-based ARchitecture). QHAR is constructed based on the idea of a Q-holon, which has four dimensions (physical, cyber, human, and energy) and can exchange three flows (energy, data, and materials). It is a generic Holon that can represent any entity or actor of the supply chain. The QHAR is structured in three levels: centralized control level, decentralized control level, and execution level. QHAR implements the principle of an oligarchical control architecture by deploying both hierarchical and heterarchical control approaches. This ensures the overall system performance and reactivity to hazards. The proposed architecture is tested and validated on a case study.

scholarly journals How environment dynamics affects production scheduling: Requirements for development of CPPS models

2021 ◽  
Vol 49 (4) ◽  
pp. 827-834
Author(s):  
Cátia Alves ◽  
Goran Putnik ◽  
Leonilde Varela
Keyword(s):  
Production Scheduling ◽  
Manufacturing Systems ◽  
New Technologies ◽  
Production Systems ◽  
Strong Positive Correlation ◽  
Systems Architecture ◽  
Study Results ◽  
Cyber Physical Production Systems ◽  
Scheduling Models

Production scheduling can be affected by many disturbances in the manufacturing system, and consequently, the feasible schedules previously defined became obsolete. Emerging of new technologies associated with Industry 4.0, such as Cyber-Physical Production Systems, as a paradigm of implementation of control and support in decision making, should embed the capacity to simulate different environment scenarios based on the data collected by the manufacturing systems. This paper presents the evaluation of environment dynamics effect on production scheduling, considering three scheduling models and three environment scenarios, through a case study. Results show that environment dynamics affect production schedules, and a very strong or strong positive correlation between environment dynamics scenarios and total completion time with delay, over three scheduling paradigms. Based on these results, the requirement for mandatory inclusion of a module for different environment dynamics scenarios generation and the corresponded simulations, of a Cyber-Physical Production Systems architecture, is confirmed.

scholarly journals Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model

2019 ◽  
Vol 9 (12) ◽  
pp. 2407 ◽  
Author(s):  
Hajo Wiemer ◽  
Lucas Drowatzky ◽  
Steffen Ihlenfeldt
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Data Acquisition ◽  
Data Analytics ◽  
Production Systems ◽  
Industrial Applications ◽  
Engineering Applications ◽  
Collaborative Project ◽  
Cyber Physical Production Systems

The value of data analytics is fundamental in cyber-physical production systems for tasks like optimization and predictive maintenance. The de facto standard for conducting data analytics in industrial applications is the CRISP-DM methodology. However, CRISP-DM does not specify a data acquisition phase within production scenarios. With this chapter, we present DMME as an extension to the CRISP-DM methodology specifically tailored for engineering applications. It provides a communication and planning foundation for data analytics within the manufacturing domain, including the design and evaluation of the infrastructure for process-integrated data acquisition. In addition, the methodology includes functions of design of experiments capabilities to systematically and efficiently identify relevant interactions. The procedure of DMME methodology is presented in detail and an example project illustrates the workflow. This case study was part of a collaborative project with an industrial partner who wanted an application to detect marginal lubrication in linear guideways of a servo-driven axle based only on data from the drive controller. Decision trees detect the lubrication state, which are trained with experimental data. Several experiments, taking the lubrication state, velocity, and load on the slide into account, provide the training and test datasets.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

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