About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Analysis of High-Speed Impact Behaviour of Al 2024 Alloy Using Machine Learning Techniques |
Author(s) |
Navya Gara, Siri S, Velmurugan R, Jayaganthan R |
On-Site Speaker (Planned) |
Navya Gara |
Abstract Scope |
The large deformation of metallic materials subjected to high-speed impact may lead to catastrophic failure of aerospace structural components fabricated using these materials. The present work is focused to analyze dynamic behavior of Al2024 alloy subjected to very high impact velocities using FEA software (LS DYNA)along with Machine Learning (ML) techniques. The transient impact behaviour of the alloy was estimated using modified Johnson- Cook visco-plastic model for a strain rate range of 100-3000/s. The residual velocities and energy absorption characteristics of Al 2024 subjected to high-speed impact estimated through FEA and analytical routes along with experimental data were utilized to train the ML models such as support vector machine, random forest, and deep neural networks for predicting the crashworthiness of structures. The comparative analysis of ML algorithms was made for its predictive accuracy in estimating the dynamic behaviour of Al 2024 alloy. |
Proceedings Inclusion? |
Undecided |