Data-Driven and Experimentally Validated Framework for Process Assurance, Machining Reliability and Surface Integrity in Tailored Heat-Treated Ni-Cr–Mo Steel Alloy Systems

Nickel–chromium–molybdenum (Ni–Cr–Mo) steel alloy systems are used in many automotive and aerospace industries for their strength, toughness, and resistance to fatigue. However, these steels are difficult to machine and tend to distort after heat treatment processes such as carburization, which increases manufacturing costs and decreases precision. This study presents an experimental and data-driven approach to improve machinability, and surface integrity, in Ni–Cr–Mo steel components. Two different heat treatment routes are compared and analyzed to reduce machining challenges and surface roughness. These include the conventional isothermal annealing (ITA) and a newly developed normalizing–subcritical annealing (NSA) process. The NSA process produced a uniform ferrite–pearlite–carbide structure that improved cutting performance and reduced property variation. Surface roughness decreased by nearly 90%, while tool life increased by 126%. The process also lowered machining cost per part by about 50%. Although strength dropped slightly, ductility and impact toughness improved by over 60%, showing a balanced mechanical response. Machine learning models, bolstered by the inclusion of explicit microstructural features, were applied to link material properties to machining outcomes. The integrated models achieved exceptional prediction accuracy, reaching [Formula: see text] for the heterogeneous ITA samples, while predictions for the refined NSA samples reached [Formula: see text] for surface roughness. The difference in model behavior reflected the variation in material uniformity. The NSA route further reduced energy consumption by 34% and CO 2 emissions by about 31%, leading to an estimated annual saving of $18,000 for 10,000 parts. The combined experimental and data-driven approach provides a practical pathway to ensure process assurance, machining reliability, and sustainable manufacturing and processing of Ni–Cr–Mo steel components, laying the groundwork for future integration into smart manufacturing and digital twin systems.