Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures
This provides a tool for optimizing AFSD processes in materials engineering, though it is incremental as it applies existing machine learning methods to a new simulation dataset.
This study tackled predicting mechanical properties like von Mises stress and logarithmic strain in Additive Friction Stir Deposited aluminum alloy structures using biomimetic machine learning, achieving high accuracy with an R square of 0.9676 for stress and 0.7201 for strain.
This study presents a novel approach to predicting mechanical properties of Additive Friction Stir Deposited (AFSD) aluminum alloy walled structures using biomimetic machine learning. The research combines numerical modeling of the AFSD process with genetic algorithm-optimized machine learning models to predict von Mises stress and logarithmic strain. Finite element analysis was employed to simulate the AFSD process for five aluminum alloys: AA2024, AA5083, AA5086, AA7075, and AA6061, capturing complex thermal and mechanical interactions. A dataset of 200 samples was generated from these simulations. Subsequently, Decision Tree (DT) and Random Forest (RF) regression models, optimized using genetic algorithms, were developed to predict key mechanical properties. The GA-RF model demonstrated superior performance in predicting both von Mises stress (R square = 0.9676) and logarithmic strain (R square = 0.7201). This innovative approach provides a powerful tool for understanding and optimizing the AFSD process across multiple aluminum alloys, offering insights into material behavior under various process parameters.