An intelligent maintenance decision-making based on cutters economic life

被引:2
|
作者
He, Jigang [1 ,2 ]
Gao, Hongli [1 ,2 ]
Li, Shichao [1 ,2 ]
Guo, Liang [1 ,2 ]
Lei, Yuncong [1 ,2 ]
Cao, Ao [1 ,2 ]
机构
[1] Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Sichuan, Peoples R China
[2] Southwest Jiaotong Univ, Engn Res Ctr Adv Driving Energy Saving Technol, Chengdu 610031, Sichuan, Peoples R China
基金
中国国家自然科学基金;
关键词
Operational life; Economic life; Joint optimization decision; Sensitivity test; Stress test; TOOL WEAR; CUTTING FORCE; MODEL; SYSTEM;
D O I
10.1016/j.ijpe.2023.109075
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The manufacturing field focuses on the accurate life of machinery equipment and cutters, that is, processing until the day before "scrap". However, this is more of an engineer's management mind than a business manager. Reallife manufacturing operations are often driven by economic gain, in other words, the purpose of production is not to obtain longer working hours of equipment (Operational life), but to minimum costs or maximize profits (Economic life). Cutters, especially expensive high-end cutters, require a lot of investment by enterprises whether to repair or replace them. Therefore, it is of great significance to find an innovative maintenance decision of their economic life. In this paper, an intelligent maintenance decision based on cutters economic life is proposed. Specifically, based on the traditional cutters' life prediction, the economic income index is introduced, and actuarial theory is integrated to explore the joint optimization decision of cutters maintenance. Meanwhile, the sensitivity test and stress test are carried out on various factors that affect the economic life of cutters, the sensitivity priority and the factors' deformations under the stress scenario are obtained. The experimental results show that the optimal time point of cutters' economic life maintenance does not necessarily occur at the optimal time point of operational life maintenance, also, this intelligent maintenance decision can significantly reduce the maintenance costs, thereby increasing the profit. Sensitivity testing and stress testing provide enterprises with risk appetite, direction focus and data support, which is conducive to Enterprise Risk Management (ERM).
引用
收藏
页数:21
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