Seminar

Abstract

Cyber-Physical System (CPS) is a crucial direction in realizing the next-generation smart manufacturing. With the deployment of IoT technologies, massive production data and information are real-timely accessible for decision-makers. However, the enemies: complexity and uncertainty, that researchers and practitioners have been fighting for decades still hinder the development of smart manufacturing. In the realm of manufacturing optimization like production planning and scheduling, considerable research efforts have been made. Yet, more or less similar results are generated, which are theoretically optimal but lack the ability to cope with practical disturbances in real-life production. This study develops a framework of the Cyber-Physical System (CPS) and proposes a smart digitalization solution to achieve real-time visibility, traceability, and information sharing. Also, a novel Spatio-Temporal Finite Element Analytics (ST-FEA) based on real-time cyber-physical visibility and traceability is innovated to achieve real-time advanced production planning, scheduling, and execution. By discretizing the space and time of the original problem in meshing, the complexity and uncertainty are substantially reduced. The simpler models can thus be established and solved with a more straightforward method. In addition, big data analytics techniques are applied to extract the hidden characteristics and patterns in CPS-enabled factories and give feedbacks and reflections of data-driven knowledge even wisdom for making improvements accordingly and finally form a closed-loop system. Case studies are conducted, and the results show that ST-FEA has a well-balanced and more stable performance. Sensitivity analyses are carried out to investigate the impacts of spatial and temporal scale of ST-Element on the performance.

Keywords: Spatio-Temporal Finite Element Analytics (ST-FEA); Cyber-Physical System (CPS); Advanced Production Planning and Scheduling (APPS); Synchronization; Smart Manufacturing