A service-oriented energy assessment system based on BPMN and machine learning

Yan, Wei, Wang, Xinyi, Gong, Qingshan, Zhang, Xumei, Zhang, Hua and Jiang, Zhigang 2022. A service-oriented energy assessment system based on BPMN and machine learning. Autonomous Intelligent Systems 2 (1) , 18. 10.1007/s43684-022-00036-0

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

Increasing energy cost and environmental problems push forward research on energy saving and emission reduction strategy in the manufacturing industry. Energy assessment of machining, as the basis for energy saving and emission reduction, plays an irreplaceable role in engineering service and maintenance for manufacturing enterprises. Due to the complex energy nature and relationships between machine tools, machining parts, and machining processes, there is still a lack of practical energy evaluation methods and tools for manufacturing enterprises. To fill this gap, a serviced-oriented energy assessment system is designed and developed to assist managers in clarifying the energy consumption of machining in this paper. Firstly, the operational requirements of the serviced-oriented energy assessment system are analyzed from the perspective of enterprises. Then, based on the establishment of system architecture, three key technologies, namely data integration, process integration, and energy evaluation, are studied in this paper. In this section, the energy characteristics of machine tools and the energy relationships are studied through the working states of machine tools, machining features of parts and process activities of processes, and the relational database, BPMN 2.0 specification, and machine learning approach are employed to implement the above function respectively. Finally, a case study of machine tool center stand base machining in a manufacturing enterprise was applied to verify the effectiveness and practicality of the proposed approach and system.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
ISSN:	2730-616X
Date of First Compliant Deposit:	23 November 2022
Date of Acceptance:	22 July 2022
Last Modified:	07 Jun 2023 23:05
URI:	https://orca.cardiff.ac.uk/id/eprint/153903

Actions (repository staff only)

Edit Item