A biomimetic machine learning approach for predicting the mechanical properties of additive friction stir deposited aluminum alloy-based walled structures

Abstract

Additive friction stir deposition (AFSD) is a solid-state manufacturing technique capable of producing high-strength, defect-free metal components. The complexity of its process parameters has driven growing interest in machine learning (ML) for improved predictive accuracy and process control. This study presents a novel biomimetic ML approach to predict the mechanical properties of AFSD-fabricated aluminum alloy-walled structures. The methodology integrates numerical modeling of the AFSD process with genetic algorithm (GA)-optimized ML 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 the complex thermal and mechanical interactions involved. A dataset of 200 samples was generated from these simulations. Decision tree and random forest (RF) regression models, optimized using GAs, were developed to predict key mechanical properties. The RF model demonstrated superior performance, achieving R² values of 0.9676 for von Mises stress and 0.7201 for logarithmic strain. This innovative approach provides a robust tool for understanding and optimizing the AFSD process across a range of aluminum alloys, offering valuable insights into material behavior under various process parameters.

Keywords

Additive friction stir deposition / Additive manufacturing / Machine learning / Hybrid algorithms

Acknowledgments

None.

Funding

None.

Conflict of interest

The author declares no competing interests.

Author contributions

This is a single-authored article.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

The data will be provided to the readers upon reasonable request.

Further disclosure

The paper has been uploaded to or deposited in arXiv preprint server: https://arxiv.org/abs/2408.05237.

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