Application of artificial neural network to identify non-random variation patterns on the run chart in automotive assembly process

Abstract

A developed methodology of using an artificial neural network to identify non-random variation patterns to improve dimensional quality in automotive assembly process is presented. The proposed pattern recognition algorithm that integrates with the process knowledge basis is designed not only to detect variation patterns, but also to address the identification of unacceptable variation manifested by non-random patterns on the control chart. Once any non-random patterns occur on the control chart, the root causes of dimensional variations can be located systematically by investigating each possible cause based on the knowledge of the assembly process. This information will help to make process modifications that reduce dimensional variability for automotive body assembly process in real time. Therefore, it can be expected that the control chart with the proposed pattern recognition algorithm will play a more important role as a systematic diagnosis tool rather than only as a statistical monitoring tool.