Prediction of Steel Plate Fault Classification Using CART Fuzzy Logic and ANFIS Models

Some systems correct faulty production to reduce the cost and increase the performance of steel plates. The first element of quality production is to identify and classify defects. However, when the error classification is not done well, the cost of correcting the error increases. This study proposes a classification and regression tree (CART) based fuzzy model to determine which fault class the faulty steel plates fall into. The second model used in the study is the adaptive neuro-fuzzy interface system (ANFIS). In both approaches, error classes were determined using fuzzy logic. In the two models created, it was observed that when the information of the detected faulty steel plate was entered, it was unsuccessful in determining which class this fault belonged to. Although it suggests a quick solution for detecting the error class, it has been seen that these approaches are not appropriate to use because they do not offer the right solution.