Possibilities of Using Specific Jominy Distance in ANN Models for Predicting Low-Alloy Steels' Microstructure.

ABSTRACT: Understanding the volume fractions of microstructure constituents such as ferrite, pearlite, bainite, and martensite in low-alloy steels is critical for tailoring mechanical properties to specific engineering applications. To address the complexity of these relationships, this study explores the use of artificial neural networks (ANNs) as a robust tool for predicting these microstructure constituents based on alloy composition, specific Jominy distance, and heat treatment parameters. Unlike previous ANN-based predictions that rely on the hardness after quenching as an input parameter, this study excludes hardness. The developed model relies on readily available input parameters, enabling accurate estimation of microstructure composition prior to heat treatment, which significantly improves

SUBMITTER: Marohnic T 

PROVIDER: S-EPMC11818817 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature