Modelling infant failure rate of electromechanical products with multilayered quality variations from manufacturing process

Abstract

The optimisation of product infant failure rate is the most important and difficult task for continuous improvement in manufacturing; how to model the infant failure rate promptly and accurately of the complex electromechanical product in manufacturing is always a dilemma for manufacturers. Traditional methods of reliability analysis for the produced product usually rely on limited test data or field failures, the valuable information of quality variations from the manufacturing process has not been fully utilised. In this paper, a multilayered model structured by ‘part-level, component-level, system-level’ is presented to model the reliability in the form of infant failure rate by quantifying holistic quality variations from manufacturing process for electromechanical products. The mechanism through which the multilayered quality variations affect the infant failure rate is modelled analytically with a positive correlation structure. Furthermore, an integrated failure rate index is derived to model the reliability of electromechanical product in manufacturing by synthetically incorporating overall quality variations with Weibull distribution. A case study on a control board suffering from infant failures in batch production is performed. Results show that the proposed approach could be effective in assessing the infant failure rate and in diagnosing the effectiveness of quality control in manufacturing.

Keywords:

• reliability analysis
• quality variations
• structured model
• infant failure rate
• Weibull distribution

Acknowledgements

The authors like to thank Prof. Xie Min for his comments and help in preparing an earlier draft of the paper. The authors would like to thank the editors and the anonymous referees for their valuable comments and suggestions for improving this paper.

Additional information

Funding

This work was supported by [grant number 61473017] from the National Natural Science Foundation of China, a theme-based research project grant [T32–101/15-R] of University Grants Council, and a Key project [number 71532008] supported by National Natural Science Foundation of China.