Abstract

This paper presents a machine learning system aimed at predicting material properties of a wide range of diversified materials, such as steels, non-ferrous 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, at various temperatures and various heat treatments and delivery conditions. The accuracy achieved in terms of relative error is in most cases above 90%, and frequently above 95%, thus being higher than readily available, statistically derived data sources such as MMPDS B-Basis values, which are routinely used in aerospace industry.