Using Principal Component Analysis and Artificial Neural Networks for Fault Type Forecasting in an Automotive Company

In this study, failures that occurred in the paint shop of an automotive company were discussed. The relationships between these failures and the probabilities of prospective occurrences were investigated. Any product produced in the company passes quality control at the end of production. Technical or operator-originated types of potential failures are examined during this control. Causes of failures in the paint shop and how they can be resolved pose a serious problem, just as in the other departments of the factory. This is because every failure encountered negatively influence the product quality and harm the company in terms of cost/productivity/image. The inability of the paint shop to predict the probability of failures in advance and its inability to establish a link between the types of failures also lead to its failure to pass quality control — which is the subsequent process — and cause its “production quality score” to fall, as well as other adversities. This study was carried out to determine which failures were usually caused by the activities in the paint shop and to develop a model that would predict the pass/fail state of the types of failures in question.