面向能耗控制的预知维护与刀具更换联合优化策略

doi:10.16183/j.cnki.jsjtu.2019.134

上海交通大学学报 ›› 2020, Vol. 54 ›› Issue (12): 1235-1243.doi: 10.16183/j.cnki.jsjtu.2019.134

面向能耗控制的预知维护与刀具更换联合优化策略

石郭, 司国锦, 夏唐斌(), 潘尔顺, 奚立峰

上海交通大学　机械系统与振动国家重点实验室；机械与动力工程学院，上海　200240

收稿日期:2019-05-14 出版日期:2020-12-01 发布日期:2020-12-31
通讯作者: 夏唐斌 E-mail:xtbxtb@sjtu.edu.cn
作者简介:石郭(1995-)，女，四川省成都市人，硕士生，主要研究方向为可持续制造与维护决策．
基金资助:
国家自然科学基金(51875359);教育部-中国移动科研基金研发项目(CMHQ-IS-201900003);临港地区智能专项(ZN2017020101)

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

SHI Guo, SI Guojin, XIA Tangbin(), PAN Ershun, XI Lifeng

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

Received:2019-05-14 Online:2020-12-01 Published:2020-12-31
Contact: XIA Tangbin E-mail:xtbxtb@sjtu.edu.cn

摘要/Abstract

摘要：

随着制造业可持续发展和节能型制造模式的逐渐兴起，针对数控机床设备和刀具的能耗控制和维护决策需求，提出能耗控制导向的机床预知维护与刀具更换联合优化策略．以非增值能耗控制为研究重点，在数控机床能耗建模中拓展引入刀具的阶段磨损规律．首先，构建机床的健康演化规律函数，规划非增值功率最小化的机床预知维护计划．然后，基于机床预知维护计划的贯序输出，建立综合节能性与经济性的刀具更换联合层全局维护优化模型，动态输出刀具与机床联合层的刀具预防更换及机床预知维护的最佳周期间隔．算例分析表明，与传统的单设备维护策略相比，机床预知维护与刀具预防更换的联合优化策略能够显著降低非增值总能耗．

关键词: 预知维护, 刀具更换, 能源消耗, 可持续制造, 联合优化

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

中图分类号:

TH17

石郭, 司国锦, 夏唐斌, 潘尔顺, 奚立峰. 面向能耗控制的预知维护与刀具更换联合优化策略[J]. 上海交通大学学报, 2020, 54(12): 1235-1243.

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.

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参考文献 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

面向能耗控制的预知维护与刀具更换联合优化策略

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

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