Part dimension deviation control in batch manufacturing process for monitoring error sources

Abstract

In a batch manufacturing process a cross-correlation exists among dimension deviations of different parts. The modelling and control of these deviations are essential to improve product dimension quality. Traditional dimension deviation statistical control methods have been focused on retrospectively analysing part dimension feature, while based on the spatial relationship between part dimensional features and error sources showed in part dimension error propagation model, this paper presents a method to control part dimension deviation in batch manufacturing that focuses on error sources. At each operation, total part deviation is separated into three components corresponding to three error sources. Therefore, a multivariate exponentially weighted moving average (MEWMA) chart for error sources is proposed to control part dimension deviations with identified error sources attribute to part dimension deviation. Efficiency and reliability of this model were verified by simulation analysis in common error source abnormal patterns, and the model is proved to be effective for detecting small deviations in a batch manufacturing process.

Keywords:

• quality control
• manufacturing process control
• dimension error propagation model
• error sources
• MEWMA

Acknowledgments

The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China, grant 70932004.