), 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:2019-05-14Online:2020-12-01Published:2020-12-31Contact: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
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