A Predictive Diffusion Model for Designing a Desensitization Heat Treatment in Steels with Cu Impurities

The high-rate recycling of scrap steel introduces persistent residual copper (Cu), which accumulates at prior austenite grain boundaries at the surface, during high-temperature reheating, leading to Cu-induced sensitization and deleterious “hot shortness”. To address this, a predictive analytical framework was derived using Fick’s Second Law and the Sekerka, Jeanfils, and Heckel (SJH) approach to model the dissolution of Cu-rich films as a 1D planar moving boundary problem. The validity of this analytical framework was first established through experimentation on controlled Cu-coated steel wire rods, where theoretical concentration profiles showed strong agreement with empirical depth profiles. When applied to a 0.21 wt.% Cu steel at 1000 °C, the model predicted a critical extinction time (t*) of approximately 8.57 min for the complete dissolution of a 20 nm sensitized film. Experimental trials on sensitized wire rods confirmed this prediction, demonstrating an 89% reduction in the frequency of detectable sensitized zones and a significant decrease in zone width following a 10 min thermal dwell. The approach provides a standardized, scalable, and composition-adaptable methodology, grounded in a 1D planar approximation, for optimizing desensitization heat treatments across a range of Cu contents, offering a practical strategy to increase scrap steel utilization while mitigating hot shortness.