A hybrid classification tree for products of complicated machines in flexible manufacturing systems

In this paper, we propose a hybrid classification tree (HCT) to classify the products of complicated machines in flexible manufacturing systems. The HCT combines a proposed clustering algorithm with the classification and regression tree (CART) to take the advantage of the constant property of control settings during any process step for a type of product. The proposed clustering algorithm split the data set into terminal clusters using splitting attributes based on a separation matrix and fuzzy rules. The terminal clusters which consist of the data of more than one product will be further classified using the CART. We have tested the HCT on the products of an ion implanter for the vast number of wafers of 26 recipes and compared the classification results and the computation time with the existing software See5 and CART. The comparison results show that the HCT retains the classification accuracy of CART while saving 40% training time.