Introduction

The importance of accurate material properties information for engineering calculations and simulations, such as CAE (Computer Aided Engineering) and FEA (Finite Element Analysis), can never be overstated. Conventional mechanical properties such as yield strength, tensile strength, hardness, and ductility may vary more than tenfold for structural steels at room temperature, depending on the variations of alloying elements, heat treatment and fabrication. With even a moderate change in working temperature, the property’s variations and changes can become even more profound and their approximation using the typical property values for some groups of alloys may lead to very serious errors.

Contemporary large material databases and material selection software can substantially help with these challenges in engineering and simulation, however it is unfortunately technically impossible to have all properties for all materials readily available from experiments and standards. Recent developments in artificial intelligence and machine learning however provide an opportunity to overcome this gap.

Machine learning (ML) has been increasingly applied in material science, including property modelling and prediction. Some examples include modelling the correlation between processing parameters of maraging steels , predicting mechanical properties of AISI 10xx steel bars [] and predicting crack propagation in stainless steels []. A comprehensive view of ML applications in material science, notably for metallic materials, is provided in [].

While possibly of interest for some particular applications and material groups, in most cases these models are not helpful to provide material properties needed for practical CAE and FEA usage. Their main flaw is the lack of needed capability to simulate the behavior of thousands of diversified structural materials from carbon and stainless steel, to special alloys, nonferrous metals and polymers. Development of such a universal system for material properties prediction would require, besides application of modern ML algorithms, an access to a large pool of datasets for the learning and testing of ML models, as well as innovative and consistent methodology to handle and curate such a quantity of data.