A generalized method for the inherent energy performance modeling of machine tools. (October 2021)

• Record Type: Journal Article
• Authors: Liu, Peiji; Zhang, Zhe; Wang, Xu; Li, Xiaobin; Wang, Xi Vincent; Tuo, Junbo
• Is Part Of: Journal of manufacturing systems. Volume 61(2021)
• Page Start: 406
• Page End: 422
• Publication Date: 2021-10
• Subjects: Energy efficiency -- Machine tools -- Inherent energy performance -- Energy-saving services; 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.2021.10.002
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A generalized method for the inherent energy performance modeling of machine tools is proposed. The framework of the inherent energy performance of machine tools are investigated to bridge the knowledge gap. Experiment design, automatic coding and data processing algorithms, are presented and integrated into a supporting system. This method can be applied to facilitate the configuration of energy-saving services among distributed machine tools. Abstract: Machine tools (MTs), as the key equipment of manufacturing systems, have enormous quantities and consume a great amount of energy. However, the diversity of both machines and their energy consumption properties make it difficult to transfer the energy-saving knowledge and services among different MT. To facilitate the initialization configuration of energy-saving services, the inherent energy performance (IEP) is investigated to describe the differences in energy consumption among MTs, and a generalized method for modeling the IEP of MT and its electrical subsystems is proposed. Three key enablers, including generalized experimental design rules, automatic coding, and data processing algorithms, are presented and integrated into a supporting system to reduce the modeling efforts and knowledge requirements. Case studies of an offline manufacturing scenario and an Internet of Things (IoT)-enabled manufacturing scenario were carried out to verify the effectiveness and convenience of the proposed method. The results show that the proposed method can provide essential modeling support for large-scale energy-saving service configurations and energy-efficient MT development.