Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures

TL;DR

This paper introduces a biomimetic machine learning framework combining finite element simulations and genetic algorithm optimization to accurately predict mechanical properties of AFSD aluminum alloy structures.

Contribution

It develops a novel integrated modeling approach that combines numerical simulations with optimized machine learning models for predicting AFSD process outcomes.

Findings

GA-RF model achieved high accuracy in stress prediction (R²=0.9676).

The approach effectively captures complex thermal-mechanical interactions.

Provides a new tool for process optimization of aluminum alloys.

Abstract

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.