Functional risk-oriented integrated preventive maintenance considering product quality loss for multistate manufacturing systems

Abstract

During the abnormal operations before breakdown of a multistate manufacturing system, product quality loss could systematically characterise the affected economic cost in advance. However, few studies have considering the valuable quality loss data, thus, a novel risk-oriented optimisation approach of integrated maintenance is proposed considering the quality loss of work-in-process (WIP) obtained from the manufacturing system. First, the functional risks in manufacturing systems are expounded and classified by detectable deviations and potential manufacturing defects of the finished product based on machine performance state, and product quality loss is therefore applied for preventive maintenance activities. Second, the degradation path of machine performance and its effect on WIP are clarified. Explicit risk is presented by dimensional deviations of key quality characteristics, whereas implicit risk is quantified by the influence of component- and system-level manufacturing defects on the early product usage phase. Third, different levels of maintenance actions are proposed, and the integrated preventive maintenance plan for the entire production horizon is obtained by minimising product quality loss and PM investments. The optimal decision combination is solved by genetic algorithm. Finally, the optimal maintenance strategy is presented to verify the superiority of this method through the analysis of a cylinder head manufacturing system.

Keywords:

• multistate manufacturing systems
• functional risks
• product quality loss
• integrated maintenance
• maintenance level

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Yihai He http://orcid.org/0000-0002-9110-2672

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [grant number 71971181] and the general project [No. JZX7Y20190242012401] funded by the National Defense Pre-Research Foundation of China.