Application of data mining and process knowledge discovery in sheet metal assembly dimensional variation diagnosis

A dimensional variation diagnostic reasoning and decision approach through the combination of data mining (DM) and knowledge discovery techniques to fulfill the “hypothesis and validation” characterized variation causes reasoning procedure.

Abstract

In the assembly of sheet metal products such as body-in-white, dimensional control is a challenging task work for quality improvement due to the complexity of both the structure and the process. This paper has developed a dimensional variation diagnostic reasoning and decision approach through the combination of data mining (DM) and knowledge discovery techniques. Correlation analysis (CA) and maximal tree (MT) methods were applied to extract the large variation group from massive multi-dimensional measured data, while the multi-variate statistical analysis (MSA) approach was used to discover the principle variation pattern. A decision tree (DT) approach based on knowledge of product and assembly process was developed to fulfill the "hypothesis and validation" characterized variation causes reasoning procedure. The established decision support analysis (DSA) approach for sheet metal assembly variation diagnosis has been proven to be effective and efficient through practical implementation.