Optimizing mean and variance of multiresponse in a multistage manufacturing process using a patient rule induction method

Abstract

Most manufacturing industries produce products through a series of sequential stages, known as a multistage process. In a multistage process, each stage is affected by its preceding stage, at the same time, it affects its following stage. Also, each stage often includes several response variables to be optimized. In this paper, we attempt to optimize the several response variables of the multistage process simultaneously considering the relationships among the stages. For this purpose, we use a particular data mining method, called a patient rule induction method. Because the relationships among the stages are often complicated, using a data mining method is a good approach for analyzing the relationships. According to the procedure of the patient rule induction method, the proposed method searches for an optimal setting of input variables directly from operational data at which mean and variance of the several response variables of the multistage process are optimized. The proposed method is explained by a step-bystep procedure using a steel manufacturing process example.