A Transfer-Learning and Continuous Optimization-Based Framework for Predicting Heat Treatment-Dependent Mechanical Properties of DED-Processed Low-Alloy Steels

Directed energy deposition (DED) of low-alloy steels involves strongly coupled effects among alloy composition, solidification behavior, and post-deposition heat treatment, making mechanical property prediction difficult when target-domain data are limited. This study develops a transfer-learning and continuous optimization framework for predicting heat treatment-dependent yield strength (YS), ultimate tensile strength (UTS), hardness (HV), and as-solidified phase fractions of martensite, ferrite, and austenite in DED-processed low-alloy steels. A CALPHAD-based dataset was generated for 125 low-alloy steel compositions. A multilayer perceptron (MLP) surrogate was first trained as a baseline model, then fine-tuned through transfer learning and progressively updated as staged continuous optimization; the composition pool increased from 72 to 125 compositions using Random, Greedy, and Bayesian upper-confidence-bound acquisition strategies. The heat treatment prediction accuracy improved from an average R2 of 0.757 for the baseline model to 0.929 after transfer learning and to approximately 0.997 after continuous optimization, with a nearly 78% reduction in RMSE relative to transfer learning. For the solidification outputs, the average R2 increased from 0.770 after transfer learning to approximately 0.859 after optimization. Bayesian-UCB provided the most stable and data-efficient improvement by balancing predicted performance with model uncertainty. The optimized prediction system showed low case-study errors for both solidification and heat treatment properties, demonstrating its potential as a rapid screening tool for alloy composition and tempering-condition selection in DED low-alloy steel development.