Monitoring of casting quality using principal component analysis and self-organizing map

Abstract

The monitoring of casting quality is very important to ensure the safe operation of casting processes. In this paper, in order to improve the accurate detection of casting defects, a combined method based on principal component analysis (PCA) and self-organizing map (SOM) is presented. The proposed method reduces the dimensionality of the original data by the projection of the data onto a smaller subspace through PCA. It uses Hotelling’s T2 and Q statistics as essential features for characterizing the process functionality. The SOM is used to improve the separation between casting defects. It computes the metric distances based similarity, using the T2 and Q (T2Q) statistics as input. A comparative study between conventional SOM, SOM with reduced data, and SOM with selected features is examined. The proposed method is used to identify the running conditions of the low pressure lost foam casting process. The monitoring results indicate that the SOM based on T2Q as feature vectors remains important comparatively to conventional SOM and SOM based on reduced data.