A New Method to Improve the Mechanism Model of Carburizing by Multivariate Linear Regression

Abstract

Heat treatment is one of the essential technologies in industrial manufacturing, and carburization is one of the processes of heat-treatment technology. With the increase of the technological requirements of production, the optimization of carburizing technology is desired. However, most carburization mechanism models are derived from the material properties and the related physical principles, which are involved huge amounts of parameters. Some parameters (such as diffusion constant, thermal conductivity, interface transfer coefficient, etc.) are difficult to measure correctly, and this is an extremely unfavorable issue to cause the results of the mechanism model inaccurate. In this paper, a new method is raised to improve the carburization mechanism model. The function of the method is to reduce the error of the model results by combining the mechanism model and the multivariate linear regression model with a small amount of sample data. At last, the author of this paper will perform experiments to prove the correctness of the method.