A strategy assisted machine learning to process multi-objective optimization for improving mechanical properties of carbon steels

Abstract Improvement in individual mechanical properties of carbon steels, such as strength or ductility, can no longer keep up with the increasingly demanding service environment. Therefore, it is of practical significance to improve two or more mechanical properties accurately and efficiently. In this work, five machine learning algorithms are first employed to establish prediction models for different mechanical properties (tensile strength, fracture strength, Charpy absorbed energy, hardness, fatigue strength, and elongation) based on the collected carbon steels data. Then, a set of mutually exclusive properties (tensile strength and elongation) and the key descriptors of the corresponding properties are identified by feature engineering, and the importance of the key materials descriptors is analyzed. The prediction models based on key descriptors for tensile strength and elongation also demonstrate good accuracy. All the key descriptors are considered as input features for the comprehensive performance (CP) calculated from the product of tensile strength and elongation. Finally, we develop a machine learning prediction model for CP and successfully apply the efficient global optimization algorithm to optimize two mutually exclusive mechanical properties. This work provides a new multi-objective optimization strategy that is expected to be used for the development of new steels with excellent comprehensive performance.