Study on hardness prediction and parameter optimization for carburizing and quenching: an approach based on FEM, ANN and GA

Abstract

A proper hardening depth is critical to the load-bearing capacity of a part, and heat treatment, including carburizing and quenching, can highly determine the hardness distribution in the part’s surface after manufacturing. This paper proposes a ‘hardness prediction and parameter optimization’ approach that deploys the finite element method (FEM), the artificial neural network (ANN), and the Genetic Algorithm (GA), to describe the relationships between the carburizing/quenching parameters and the hardening depths and conversely to determine the optimized parameters for a given hardening depth. First, the numerical models for carburizing, quenching, and the hardness field are built respectively. And based on these models, the finite element simulation model is designed to predict the carbon content, the microstructure and the hardness of the part. A BP network is then trained by using the data obtained from the finite element simulation, and the model between the carburizing/quenching parameters and the hardening depths on part is established. The optimization model for the carburizing/quenching parameters is finally established through GA, which can determine the optimized parameters for a given hardening depth. The effectiveness of the ‘prediction-optimization’ approach is verified by a series of experiments. The hardening depth predicted by the proposed approach holds a 10% relative error from that measured in the carburizing and quenching experiment. And the optimized parameters for the heat treatment process can work as a meaningful reference for the heat treatment.

Keywords

• hardening depth
• parameter optimization
• microstructure
• carburizing
• quenching