CALPHAD-based alloy design and strength prediction of high-performance steels under QLT processing

A predictive alloy design strategy integrating CALPHAD-based thermodynamics and hardness modeling was developed to design high-performance steels processed via quenching–lamellarization–tempering (QLT). Traditional empirical optimization of QLT steels requires extremely precise temperature control, which limits industrial scalability. In this study, a quantitative composition–strength prediction model was established by combining thermodynamic calculations with a dual-phase hardness model incorporating tempered martensite and intercritical ferrite. The model successfully predicts tensile strength with an error below 10%, identifying carbon, silicon, and manganese as key strengthening elements, while silicon uniquely broadens the intercritical annealing temperature window. Guided by this model, an optimized steel composition was designed and experimentally validated, achieving yield strength above 820 MPa, tensile strength above 1000 MPa, and Charpy impact energy exceeding 120 J at –40 °C. The results confirm the capability of the model to ensure robust mechanical performance under realistic process variations, enabling reliable industrial application of QLT processing. This integrated computational–experimental framework provides a practical and efficient pathway for composition optimization of high-performance steels, promoting the transition of alloy design from empirical trial-and-error toward predictive, data-driven methodology.