Application of a sparse mixed regression method to design the optimal composition and heat treatment conditions for transformation-induced plasticity steel with high strength and large elongation

The chemical compositions and heat treatment conditions for obtaining low-alloyed transformation-induced plasticity (TRIP) steel were designed using a sparse mixed regression method (SMRM) that automatically and spontaneously selected several regression models. The input data concerning the elemental compositions (Fe, C, Si, Mn, Cr, Ni) and austempering conditions were collected from published reports. The SMRM-based analysis of these data established three models, which each proposed conditions for the alloy and process for obtaining high strength and large elongation. Experimental results confirmed that all three sets of conditions provided bainite with retained austenite, which was necessary for the low-alloyed TRIP steel. The tensile tests revealed that all three cases exhibited strengths >1.6 GPa and elongations >11%. Evaluating the metallurgical parameters highlighted two different design concepts, in one of which key factor is to reduce the Mn content, which is different from most of the conventional approach for preparing low-alloyed TRIP steel.

Automated summary

This study designed the chemical compositions and heat treatment conditions for low-alloyed TRIP steel using a sparse mixed regression method. Experimental results confirmed that these conditions provided high strength and large elongation. Evaluating the metallurgical parameters highlighted two different design concepts.