Process parameter optimization for lifetime improvement experiments considering warranty and customer satisfaction. (May 2022)

• Record Type: Journal Article
• Authors: Wang, Guodong; Shao, Mengying; Lv, Shanshan; Kong, Xiangfen; He, Zhen; Vining, Geoff
• Is Part Of: Reliability engineering & system safety. Volume 221(2022)
• Publication Date: 2022-05
• Subjects: Robust parameter design -- Reliability improvement -- Taguchi loss function -- Weibull distribution -- Warranty period -- Design lifetime; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108369
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: We improve product lifetime and reduce variance simultaneously;. Design lifetime and warranty period are introduced to measure the loss cost;. The factor levels are determined considering the loss of customer and manufacturer;. The optimal solution is different for different design lifetime and warranty period. Abstract: Many process parameters may affect product reliability significantly. Manufacturers need to identify the key processes and optimize their parameter levels. Traditional methods only improve product lifetime through maximizing the location parameter. They ignore the robustness of the product. In this article, we improve product lifetime and reduce variance simultaneously by changing the levels of experimental factors. Considering warranty and customer satisfaction, we introduce a new loss function, and get the average loss cost of manufacturer and customer. We obtain the optimal levels of experimental factors through minimizing the total loss cost. We illustrate the proposed method using a real industrial experiment. The results show that the optimal solution is changed with warranty period and design lifetime. Furthermore, we explain the changes of experimental factors, and discuss some related issues in actual application scenarios.