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Optimization of Cooling Rate for Bainite Evolution in AHSS Using Machine Learning

Mechanical properties of advanced high-strength steel (AHSS) are linked to its microstructure, influenced by processing techniques during production, specifically by hot rolling thermomechanical processing. In this article, a novel adaptive machine learning (ML) model coupled with controlled cooling of hot-rolled plates was developed to predict bainite in AHSS. A neural network model of the time-temperature-transformation diagram was used at each cooling step to predict continuous-cooling-transformation kinetics. To verify the bainite fraction, dilatometry experiments were performed with AHSS specimens cooled at rates from 0.1 to 10 degree Celsius/second. An adaptive-ML model for bainite was trained using inputs from experiments and simulation, offering a predictive tool for optimizing AHSS processing.
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2025/10 AIST Iron & Steel Technology October
PR-PM1025-3
Henry Haffner, Barshan Saha, K. Chandrashekhara, Mario Buchely, Simon Lekakh, Ronald O'Malley
October 01, 2025
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