Modeling the steel case carburizing quenching process using statistical and machine learning techniques

Simulation of various manufacturing processes such as heat treatments is rapidly gaining importance in the industry for process optimization, enhancing efficiency and improving product quality. Case carburization followed by quenching is one such significant heat treatment process commonly used in the automotive industry. The equations to be solved for simulation of these processes are non-linear differential equations and require the use of computationally intensive numerical techniques e.g. 3D Finite Element Modelling. Using these models for solving optimization or inverse problems, compounded by the fact that a large number of evaluations need to be carried out becomes computationally expensive. This necessitates a simpler, computationally inexpensive representation of the process, albeit being applicable to a limited range of process parameters and conditions. In this paper, we explore the use of proven statistical techniques such as Linear Regression and machine learning techniques such as Artificial Neural Networks and Genetic Programming to create computationally inexpensive surrogate models of the carburization quenching processes to predict surface hardness and their results are presented.