This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

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. While large material databases and material selection software can help with these challenges in engineering and simulation, it is unfortunately technically impossible to have all properties for all materials readily available from experiment and standards. Recent developments in artificial intelligence and machine learning however provide an opportunity to overcome this gap. This paper presents a machine learning system aimed at predicting material properties of a wide range of diversified materials, such as stainless steels, aluminums, coppers, refractory alloys and polymers. By using copious training sets provided from a very large database and proprietary methodology for taxonomy, data curation and normalization, the developed system is able to predict physical and mechanical properties for hundreds of thousands of materials, on various temperatures and various heat treatments and delivering conditions. The accuracy achieved in the terms of relative error is in most cases above 90%, and frequently above 95%, thus being clearly higher than MMPDS B-Basis values, which are used in aerospace industry.