Process optimization of quenching and partitioning by machine learning aided with orthogonal experimental design

Abstract

Owing to a balance between toughness and strength, quenching and partitioning (Q&P) is promising in steel industry. However, for a new material or a new process, it remains challenging how to get the best parameters in low cost way. Here, a novel workflow combining orthogonal experimental design with artificial neural network and particle swarm optimization, was adopted to explore the relationship between quenching and partitioning process parameters and properties in Fe-0.65 wt%C-1.50 wt%Si-0.91 wt%Mn-1.08 wt%W steel. By using this method, the workload is reduced significantly. Compared with traditional process, the elongation of the steel increases by 146% times without loss in yield strength and a little improvement in ultimate tensile strength by quenching at 167 °C followed by partitioning at 367 °C for 5.0 min.

Keywords

• quenching and partitioning
• orthogonal experimental design
• machine learning
• particle swarm optimization
• process optimization