Research on CBR-based intelligent decision-making method for final heat treatment process of new energy heavy truck gear

Abstract

In order to ensure that the center and surface of the new energy heavy truck gear can meet different performance requirements, the final heat treatment process is carburization and low temperature tempering after quenching, which involves the determination of many process parameters. At present, these parameters are mainly determined manually, but the trial and error period is too long to ensure the effect. In order to solve this problem, this study proposes an intelligent decision-making method. On the basis of accurately describing the local similarity of process characteristic parameters, information entropy is introduced to express the impact of process characteristic parameters on decision-making, and entropy weight method is used to objectively weight process characteristic parameters. Then, the global similarity of process characteristic parameters is determined according to their local similarity and weight. The practical application shows that this method can realize the intelligent decision-making of the final heat treatment process parameters of new energy heavy truck gears.