Steel Development and Optimization Using Response Surface Models

Abstract

There is a significant potential to increase efficiency and focus in steel development with more advanced and sophisticated technologies. Response surface models are thereby introduced into this field to integrate ‘big data’ and computationally bridge inputs to outputs. In this work, a completed procedure is presented to show training response surface models using different algorithms based on a steel chemistry and processing database with corresponding mechanical properties. Furthermore, optimization is applied to mine feasible but undeveloped new steel possibilities from the well-trained response surface model. To validate the computation, a laboratory steel is processed, and the resulting mechanical properties are compared with the computational results.