Task offloading in cloud-edge collaboration-based cyber physical machine tool. (February 2023)

• Record Type: Journal Article
• Authors: Wang, Chuting; Guo, Ruifeng; Yu, Haoyu; Hu, Yi; Liu, Chao; Deng, Changyi
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 79(2023)
• Publication Date: 2023-02
• Subjects: Cyber-physical machine tool -- Digital twin -- Cloud-edge collaboration -- Task offloading; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102439
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: Proposes an architecture for cloud edge collaboration-based CPMT. Proposes a task offloading technique to improve responsiveness. Proposes an offloading algorithm to provide load balancing while maintaining system real-time and throughput rate requirements. An application case is given for PHM of cutting tool. Abstract: The Cyber-Physical Machine Tool (CPMT) is a promising solution for the next generation of machine tool digitalization and servitization due to its excellent interconnection, intelligence, adaptability, and autonomy. The rapid development of next-generation information technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), provided richer services for CPMT but also led to problems of idle on-site computing resources, and excessive pressure on the cloud, slow service response and poor privacy. To solve the above problems, this paper proposes a cloud-edge collaboration-based CPMT architecture, which makes full use of the computing resources of existing devices in the industrial sites, offloads digital twin (DT) modeling and data processing from the cloud to the edge, and provides microservice interfaces for users at the edge. Given the limited computing resources available in the field and the demand for latency-sensitive applications, task offloading methods aimed at response speed and load balancing are proposed, respectively. Finally, a case of machine tool Prognostics and Health Management (PHM) service is presented, in which the proposed method is used to perform tool wear monitoring, prediction, and health management.