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

doi:10.16183/j.cnki.jsjtu.2019.134

Abstract

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

CLC Number:

TH17

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 Jiao Tong University, 2020, 54(12): 1235-1243.

Figures/Tables 9

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References 15

[1]	刘飞，刘培基，李聪波，等. 制造系统能量效率研究的现状及难点问题[J]. 机械工程学报，2017, 53(5): 1-11.
	LIU Fei, LIU Peiji, LI Congbo, et al. The statue and difficult problems of research on energy efficiency of manufacturing systems[J]. Journal of Mechanical Engineering, 2017, 53(5): 1-11.
[2]	郝虹斐，郭伟，桂林，等. 非完美维修情境下的预防性维修多目标决策模型[J]. 上海交通大学学报，2018, 52(5): 518-524.
	HAO Hongfei, GUO Wei, GUI Lin, et al. A multi-objective preventive maintenance decision-making model for imperfect repair process[J]. Journal of Shanghai Jiao Tong University, 2018, 52(5): 518-524.
[3]	孙博文，郭闻雨，夏唐斌，等. 面向串并联生产系统机会维护的产能平衡导向租赁利润优化策略[J]. 上海交通大学学报，2019, 53(3): 276-284.
	SUN Bowen, GUO Wenyu, XIA Tangbin, et al. Capacity balancing-oriented leasing profit optimization of opportunistic maintenance for leased series-parallel production system[J]. Journal of Shanghai Jiao Tong University, 2019, 53(3): 276-284.
[4]	XIA T B, DONG Y F, XIAO L, et al. Recent advances in prognostics and health management for advanced manufacturing paradigms[J]. Reliability Engineering & System Safety, 2018, 178: 255-268.
[5]	DU S C, XU R, LI L. Modeling and analysis of multiproduct multistage manufacturing system for quality improvement[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(5): 801-820.
[6]	王秋莲，刘飞. 数控机床多源能量流的系统数学模型[J]. 机械工程学报，2013, 49(7): 5-12.
	WANG Qiulian, LIU Fei. Mathematical model of multi-source energy flows for CNC machine tools[J]. Journal of Mechanical Engineering, 2013, 49(7): 5-12.
[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[J]. Journal of Cleaner Production, 2016, 112: 3721-3734.
[8]	王强，赵刚，阮丹，等. 基于工艺参数响应曲面的FDM 3D打印机能耗分析[J]. 组合机床与自动化加工技术，2018, 533(7): 153-156.
	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[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2018, 533(7): 153-156.
[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[J]. Journal of Cleaner Production, 2015, 101: 337-347.
[10]	YAN J H, HUA D G. Energy consumption modeling for machine tools after preventive maintenance[C]∥IEEE International Conference on Industrial Engineering and Engineering Management. Macao, China: IEEE, 2010: 2201-2205.
[11]	HOANG A, DO P, LUNG B. Investigation on the use of energy efficiency for condition-based maintenance decision-making[J]. IFAC-PapersOnLine, 2016, 49(28): 73-78.
[12]	XIA T B, XI L F, DU S C, et al. Energy-oriented maintenance decision-making for sustainable manufacturing based on energy saving window[J]. Journal of Manufacturing Science and Engineering Transactions of the ASME, 2018, 140(5): 051001.
[13]	MOURTZIS D, VLACHOU E, MILAS N, et al. A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring[J]. Procedia CIRP, 2016, 41: 655-660.
[14]	DENKENA B, BLUEMEL P, KROENING S, et al. Condition based maintenance planning of highly productive machine tools[J]. Production Engineering, 2012, 6(3): 277-285.
[15]	赵国刚，赵亚伟，王英. 磨损能耗的测定及其分析模型的建立[J]. 实验技术与管理，1996, 13(4): 43-46.
	ZHAO Guogang, ZHAO Yawei, WANG Ying. Determination of wear energy consumption and establishment of analytical model[J]. Experimental Technology and Management, 1996, 13(4): 43-46.

刀具磨损及能耗参数		机床可靠性参数		机床与刀具维护参数
变量	取值	变量	取值	变量	取值
v₀	500	β	2	P_Pij	40
δ	1.2	η	2 000	P_Vij	70
w	0.05	a_ij	0.05	P_Rij	89
δ	0.01	b_ij	1.05	C_Pij	200
c_ij	1.2	ε_ij	1.035	C_Rij	800
d_ij	1.1			C_Vij	600
f_ij	0.28			C_Cij	2 000
α	0.22			T_Pij	18
φ	1.3			T_Rij	40

j	P_1j/kW
1	9.260	867	849
2	11.299	814	1 663
3	12.757	743	2 406
4	14.404	677	3 083
5	16.321	617	3 700
6	18.567	562	4 262
7	21.202	511	4 773

权值组合
CASE1	6	14.094	1.973	1.000	4 280
CASE2	3	11.593	2.181	1.150	2 424
CASE3	1	9.710	3.576	1.000	867

参数	取值
	848
	794
	744
	698
	655
	616
	579
	544
	512
E_TNV/(kW·h)	99 112
C_TNV/美元	13 021