Task offloading in cloud-edge collaboration-based cyber physical machine 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.

Publication DOI: https://doi.org/10.1016/j.rcim.2022.102439
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Additional Information: © 2022 Elsevier Ltd. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Cloud-edge collaboration,Cyber-physical machine tool,Digital twin,Task offloading,Control and Systems Engineering,Software,Mathematics(all),Computer Science Applications,Industrial and Manufacturing Engineering
Publication ISSN: 0736-5845
Last Modified: 01 Mar 2024 08:16
Date Deposited: 06 Sep 2022 15:08
Full Text Link:
Related URLs: https://www.sci ... 736584522001235 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-02
Published Online Date: 2022-08-23
Accepted Date: 2022-08-10
Authors: Wang, Chuting
Guo, Ruifeng
Yu, Haoyu
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Deng, Changyi

Export / Share Citation


Statistics

Additional statistics for this record