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 ◽  
...  

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.

2000 ◽  
Vol 6 (4) ◽  
pp. 321-357 ◽  
Author(s):  
S.-Y. Chiang ◽  
C.-T. Kuo ◽  
J.-T. Lim ◽  
S. M. Meerkov

This work develops improvability theory for assembly systems. It consists of two parts. Part I includes the problem formulation and the analysis technique. Part II presents the so-called improvability indicators and a case study.Improvability theory addresses the questions of improving performance in production systems with unreliable machines. We consider both constrained and unconstrained improvability. In the constrained case, the problem consists of determining if there exists a re-distribution of resources (inventory and workforce), which leads to an increase in the system's production rate. In the unconstrained case, the problem consists of identifying a machine and a buffer, which impede the system performance in the strongest manner.The investigation of the improvability properties requires an expression for the system performance measures as functions of the machine and buffer parameters. This paper presents a method for evaluating these functions and illustrates their practical utility using a case study at an automotive components plant. Part II uses the method developed here to establish conditions of improvability and to describe additional results of the case study.


2021 ◽  
Vol 49 (4) ◽  
pp. 827-834
Author(s):  
Cátia Alves ◽  
Goran Putnik ◽  
Leonilde Varela

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.


2019 ◽  
Vol 9 (22) ◽  
pp. 4811 ◽  
Author(s):  
Dong He ◽  
Qingyu Xiong ◽  
Xuyang Zhang ◽  
Yunchuang Dai ◽  
Ziyan Jiang

This paper presents a novel control system for chiller plants that is decentralized and flat-structured. Each device in chiller plant system is fitted with a smart node. It is a smart agent, which collects, handles and sends out information to its neighbours. All the smart nodes form a network that can realize self-organization and self-recognition. Different kinds of control strategies can be converted into series of decentralized computing processes carried on by the smart nodes. The principle and mechanism of this decentralized, flat-structured control system for chiller plants are described in detail. Then a case study is presented to show how to build the decentralized, flat-structured control system actually. The measured data shows that the decentralized control method is energy efficiency. Moreover, it is much more flexible and scalable compared with the traditional centralized control method.


2018 ◽  
Vol 7 (1) ◽  
pp. 131-142 ◽  
Author(s):  
Ralf Heynicke ◽  
Dmytro Krush ◽  
Christoph Cammin ◽  
Gerd Scholl ◽  
Bernd Kaercher ◽  
...  

Abstract. In the context of the Industry 4.0 initiative, Cyber-Physical Production Systems (CPPS) or Cyber Manufacturing Systems (CMS) can be characterized as advanced networked mechatronic production systems gaining their added value by interaction with the ambient Industrial Internet of Things (IIoT). In this context appropriate communication technologies and standards play a vital role to realize the manifold potential improvements in the production process. One of these standards is IO-Link. In 2016 more than 5 million IO-Link nodes have been produced and delivered, still gaining increasing acceptance for the communication between sensors, actuators and the control level. The steadily increasing demand for more flexibility in automation solutions can be fulfilled using wireless technologies. With the wireless extension for the IO-Link standard, which will be presented in this article, maximum cycle times of 5 ms can be achieved with a probability that this limit will be exceeded to be at maximum one part per billion. Also roaming capabilities, wireless coexistence mechanisms and the possibility to include battery-powered or energy-harvesting sensors with very limited energy resources in the realtime network were defined. For system planning, setup, operation and maintenance, the standard engineering tools of IO-Link can be employed so that the backward compatibility with wired IO-Link solutions can be guaranteed. Interoperability between manufacturers is a key requirement for any communication standard, thus a procedure for IO-Link Wireless testing is also suggested.


2019 ◽  
Vol 9 (12) ◽  
pp. 2407 ◽  
Author(s):  
Hajo Wiemer ◽  
Lucas Drowatzky ◽  
Steffen Ihlenfeldt

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.


2006 ◽  
Vol 2006 ◽  
pp. 1-30 ◽  
Author(s):  
Shu-Yin Chiang

This paper develops the procedure for the analysis of the production systems with quality control devices. The evaluation of the production system requires an expression for the system performance measures as functions of the machine and buffer parameters. This paper presents a method for evaluating these functions and illustrates their practical utility using a case study at a production plant.


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