Cloud-edge orchestration-based bi-level autonomous process control for mass individualization of rapid printed circuit boards prototyping services. (April 2022)

• Record Type: Journal Article
• Authors: Leng, Jiewu; Chen, Ziying; Sha, Weinan; Ye, Shide; Liu, Qiang; Chen, Xin
• Is Part Of: Journal of manufacturing systems. Volume 63(2022)
• Page Start: 143
• Page End: 161
• Publication Date: 2022-04
• Subjects: Mass individualization -- Blockchained multi-agent system -- Cloud-edge orchestration -- Autonomous process control -- Blockchained smart contracts; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2022.03.008
• Languages: English
• ISSNs: 0278-6125

Abstract

Abstract: Rapid printed circuit boards (PCB) prototyping refers to the pilot production of PCB before mass production for verifying the circuit design. The rapid PCB prototyping service providers are pursuing the mass individualization paradigm. Yet, these providers suffer from low efficiency and high cost in the large-scale small-batched mixed pilot production of rapid PCB prototyping. To improve the production efficiency in mass individualization of rapid PCB prototyping, this paper proposed a cloud-edge orchestration-based bi-level autonomous process control (CEO-BAPC) framework. A blockchained smart contracts-driven multi-agent system is established at the edge to realize efficient tasks coordination under disturbances. Based on the operation events data and control decisions collected at the edge, a customized deep learning-driven prediction model is established at the cloud for supporting the rescheduling decisions. The blockchained smart contracts (e.g., Stackelberg game solution) at the edge proactively decentralize short-term fine-grained individualized task execution among manufacturing units/machines and make the results available on upper-level deep learning model at the cloud for supporting holistic rescheduling decisions. The bi-level autonomous process control architecture avoids the inconsistency between holistic control and local execution under frequent random disturbances and thereby realizes efficient manufacturing of mass individualization. Through a simulated case based on data collected from a rapid PCB prototyping service provider in China, the feasibility and efficiency of the proposed CEO-BAPC framework are verified. Highlights: A cloud-edge orchestration-based bi-level autonomous process control (CEO-BAPC) framework. A blockchained smart contracts-driven multi-agent system for tasks coordination under disturbances. Stackelberg game-based autonomous process control for managing the production loading.