Product Quality Information–Based Integrated Maintenance Strategies for Stochastic Manufacturing System Considering Implicit and Explicit Risks

With the rapid development of smart manufacturing, the joint optimization of production, equipment maintenance, and product quality control has become key to improving manufacturing system performance. To formulate maintenance strategies for manufacturing systems, most studies use equipment degradation level as an indicator of whether maintenance actions should be initiated. However, product quality often deteriorates before equipment degradation is detected. This study develops a new joint optimization model based on quality information. First, we quantify a production quality risk indicator based on the deviation of key quality characteristics and potential risk and formulate a preventive maintenance strategy on that basis. Then, we construct a dual-objective optimization model using Pareto frontier analysis that accounts for both cost minimization and equipment availability in cycle maximization, aiming to improve the accuracy of maintenance strategies. Finally, the effectiveness of the proposed method is demonstrated through specific examples and sensitivity analysis, as well as a comparative study. The results show that the proposed method achieves the lowest total cost among the considered policies, while keeping a high availability comparable to existing policies. This outcome can provide decision support for the high-reliability, low-cost operation and maintenance of smart manufacturing systems.