An investigation into the method of energy monitoring and reduction for machining systems. (October 2020)

• Record Type: Journal Article
• Authors: Hu, Luoke; Zheng, Hao; Shu, Lianjie; Jia, Shun; Cai, Wei; Xu, Kangkang
• Is Part Of: Journal of manufacturing systems. Volume 57(2020)
• Page Start: 390
• Page End: 399
• Publication Date: 2020-10
• Subjects: 3-D three-dimension -- 5-D five-dimension -- BDA big data analytics -- CPS cyber-physical systems -- DEM digital energy meter -- DEMs digital energy meters -- DT digital twin -- EC energy consumption -- IoT Internet of Things -- MSs machining systems -- RFID Radio Frequency IDentification -- RRE RFID read event -- SchS scheduling scheme; Five-dimension model -- Energy efficiency -- Machining systems -- Energy monitoring -- Energy simulation -- Sustainable manufacturing; 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.2020.10.012
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A five-dimension model for energy consumption of machining systems is developed. The automatic acquisition of energy data is driven by Radio Frequency IDentification. Virtual spaces interact with physical spaces to calibrate the machining energy simulation. Abstract: Improving the energy efficiency of machining systems (MSs) is an urgent and challenging issue in sustainable manufacturing. Considering the dynamics and uncertainty of MSs, a five-dimension model is developed to monitor and reduce the energy consumption (EC) of MSs. In this model, the automatic acquisition of EC data from physical MSs is driven by Radio Frequency IDentification (RFID) read events. The EC service model provides monitoring and scheduling services for reducing the EC of physical MSs by connecting with virtual MSs in real time. The procedures of the EC reduction method for the MSs are presented. A case study of MSs for auto parts was conducted to illustrate the proposed method. The results showed that the time and EC of MSs were reduced by 148 s and 49.67 KJ, respectively.