Sustainable ductile iron design: Leveraging machine learning and genetic algorithms for critical raw materials reduction

One of this century's key challenges is ensuring the supply of critical raw materials essential for economic stability. These materials face supply risks due to factors like scarcity, geopolitical instability in supplier countries, and lack of substitutes. The European Union maintains a list of critical raw materials, updated every three years, and is strongly committed to developing strategies to mitigate these supply risks. Although ductile iron is a strategic material for mass production, offering low cost and high mechanical performance, its production requires critical raw materials, as well, such as Ti, Mg, Nb, and Sb, among others. To address this issue, researchers are exploring substitutes that could replace critical raw materials in ductile iron's composition without compromising or potentially even improving its mechanical properties. In this work, a deep neural networks-based model has been developed to assess the mechanical properties of ductile cast iron based on its chemical composition. The model has then been integrated with a genetic algorithm to optimize the chemical composition of the alloy in a critical raw material perspective, while maintaining equivalent mechanical performance. Three case studies were presented to illustrate the potentialities of the proposed approach.