Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2020 , Vol. 54 >Issue 12: 1235 - 1243

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2019.134

Joint Optimization Strategy of Predictive Maintenance and Tool Replacement for Energy Consumption Control

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  • School of Mechanical Engineering; State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2019-05-14

  Online published: 2020-12-31

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Abstract

With the rise of sustainable development and energy-saving mode in the manufacturing industry, a joint optimization strategy of machine predictive maintenance and tool replacement is proposed aiming to meet the needs of energy control and maintenance decision for computer numerical control (CNC) machine and tools. The non-value-added energy consumption is taken as the research emphasis, while the phased tool wear evolution is introduced into the energy modeling of the CNC machine. First, predictive maintenance (PM) scheduling of the CNC machine based on healthy evolution aims to achieve the minimization of the non-value-added power. Secondly, based on the sequential outputs of the CNC machine PM intervals, a joint replacement model of the tool is also established considering comprehensive energy saving and economy. The optimal cycle interval of tool preventive replacement and the CNC machine PM is obtained in the joint optimization layer. The case study analysis shows that compared with the traditional maintenance strategies, this joint optimization strategy can significantly reduce the total non-value-added energy consumption.

Key words： predictive maintenance (PM); tool replacement; energy consumption; sustainable manufacturing; joint optimization

Cite this article

SHI Guo, SI Guojin, XIA Tangbin, PAN Ershun, XI Lifeng . Joint Optimization Strategy of Predictive Maintenance and Tool Replacement for Energy Consumption Control[J]. Journal of Shanghai Jiaotong University, 2020 , 54(12) : 1235 -1243 . DOI: 10.16183/j.cnki.jsjtu.2019.134

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