Optimisation of burn-in time considering the hidden loss of quality deviations in the manufacturing process. Issue 10 (19th May 2017)

• Record Type: Journal Article
• Authors: He, Yihai; Wang, Linbo; Wei, Yi; He, Zhenzhen
• Is Part Of: International journal of production research. Volume 55:Issue 10(2017)
• Page Start: 2961
• Page End: 2977
• Publication Date: 2017-05-19
• Subjects: quality deviations -- quality loss -- manufacturing defects -- defect growth -- burn-in test; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2016.1262081
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : The burn-in test time is an important parameter of the complex batch processing machine scheduling problem. The omission of the loss of quality deviations in manufacturing generates a non-comprehensive and imperfect result in the optimisation of burn-in time, which hinders the identification of proactive and economical optimisation strategies to prevent infant failure in manufacturing. To solve this problem, this study visualises and quantifies for the first time the hidden loss caused by quality deviations in manufacturing and uses it as a newly added constraint to optimise the burn-in time. Firstly, a quality loss model composed of visible yield loss and warranty costs related to measurable but undetectable reliability vulnerabilities is defined. Secondly, the loss effects of growing defects are measured during the burn-in test, and the optimal burn-in time expressed by the proposed quality loss model is traded off between the additional burn-in cost and the decreased quality loss for an acceptable low infant failure rate. Finally, the effectiveness of the proposed optimisation approach is demonstrated using actual data from a control board with a high infant failure rate. Results show that the proposed method can systematically combine the fundamental loss of quality deviations in the optimisation of burn-in time, which supplements the commonly used optimality criteria, with the upstream loss of quality deviations in the form of manufacturing defects.