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

被引：0

作者：

Shi G. ^{[1
]}

Si G. ^{[1
]}

Xia T. ^{[1
]}

Pan E. ^{[1
]}

Xi L. ^{[1
]}

机构：

[1] School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2020年 / 54卷 / 12期

关键词：

Energy consumption; Joint optimization; Predictive maintenance (PM); Sustainable manufacturing; Tool replacement;

D O I：

10.16183/j.cnki.jsjtu.2019.134

中图分类号：

学科分类号：

摘要：

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. © 2020, Shanghai Jiao Tong University Press. All right reserved.

引用

页码：1235 / 1243

页数：8

共 15 条

[1]

LIU Fei, LIU Peiji, LI Congbo, Et al., The statue and difficult problems of research on energy efficiency of manufacturing systems, Journal of Mechanical Engineering, 53, 5, pp. 1-11, (2017)

[2]

HAO Hongfei, GUO Wei, GUI Lin, Et al., A multi-objective preventive maintenance decision-making model for imperfect repair process, Journal of Shanghai Jiao Tong University, 52, 5, pp. 518-524, (2018)

[3]

SUN Bowen, GUO Wenyu, XIA Tangbin, Et al., Capacity balancing-oriented leasing profit optimization of opportunistic maintenance for leased series-parallel production system, Journal of Shanghai Jiao Tong University, 53, 3, pp. 276-284, (2019)

[4]

XIA T B, DONG Y F, XIAO L, Et al., Recent advances in prognostics and health management for advanced manufacturing paradigms, Reliability Engineering & System Safety, 178, pp. 255-268, (2018)

[5]

DU S C, XU R, LI L., Modeling and analysis of multiproduct multistage manufacturing system for quality improvement, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48, 5, pp. 801-820, (2018)

[6]

WANG Qiulian, LIU Fei, Mathematical model of multi-source energy flows for CNC machine tools, Journal of Mechanical Engineering, 49, 7, pp. 5-12, (2013)

[7]

ZHOU L R, LI J F, LI F Y, Et al., Energy consumption model and energy efficiency of machine tools: A comprehensive literature review, Journal of Cleaner Production, 112, pp. 3721-3734, (2016)

[8]

WANG Qiang, ZHAO Gang, RUAN Dan, Et al., A study on the energy consumption of FDM 3D printer based on response surface of machining parameters, Modular Machine Tool & Automatic Manufacturing Technique, 533, 7, pp. 153-156, (2018)

[9]

LIN W W, YU D Y, ZHANG C Y, Et al., A multi-objective teaching-learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint, Journal of Cleaner Production, 101, pp. 337-347, (2015)

[10]

YAN J H, HUA D G., Energy consumption modeling for machine tools after preventive maintenance, IEEE International Conference on Industrial Engineering and Engineering Management, pp. 2201-2205, (2010)

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