Microstructure-Informed Prediction of Transformation Products in C-Mn and C-Mn-Nb Steels for Data-Driven Process-Window Design

Controlling the final microstructure of C-Mn and microalloyed C-Mn-Nb steels requires understanding how the prior austenite state and cooling path determine the transformation products, morphology, and mechanical response. In this work, a microstructure-informed prediction framework was developed to evaluate which transformation products can be predicted from experimentally quantified austenite descriptors within a defined industrial processing domain. A thermomechanical matrix of 80 specimens was combined with correlative light optical, scanning electron, and electron backscatter diffraction microscopy to quantify the prior austenite grain size, axial ratio, dislocation density, and cooling rate as the inputs, and the phase fractions, morphology descriptors, and hardness as the targets. The target-specific regression models from linear, kernel-based, tree-based, and gradient-boosting families were evaluated against a dummy regressor baseline using cross-validation. Reliable quantitative predictions were obtained for ferrite, pearlite, pearlite mean free path length, final size descriptor, and hardness, while the predictions for martensite, Widmanstätten ferrite, and individual bainitic subclasses remained limited by sparse occurrence and overlapping transformation windows. The framework is therefore proposed as a microstructure-informed process-window screening and experiment prioritization tool rather than a universal transformation model, with a closed-data transparency strategy enabling critical evaluation under industrial confidentiality constraints.