Summary of Biomimetic Machine Learning Approach For Prediction Of Mechanical Properties Of Additive Friction Stir Deposited Aluminum Alloys Based Walled Structures, by Akshansh Mishra
Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures
by Akshansh Mishra
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study combines numerical modeling of Additive Friction Stir Deposited (AFSD) processes with machine learning models to predict mechanical properties of aluminum alloy walled structures. The approach optimizes genetic algorithm-optimized machine learning models using finite element analysis simulations of five aluminum alloys, capturing thermal and mechanical interactions. A dataset of 200 samples is generated from these simulations, which are then used to develop Decision Tree (DT) and Random Forest (RF) regression models for predicting von Mises stress and logarithmic strain. The GA-RF model shows 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special machines to make predictions about how metal structures will behave under different conditions. It uses a combination of computer simulations and machine learning algorithms to predict things like stress and strain in these structures. The researchers used five different types of aluminum alloy and simulated different processes that would be used to create the structures. They then developed models using decision trees and random forests, which were optimized using genetic algorithms. These models were able to accurately predict the behavior of the metal structures. |
Keywords
* Artificial intelligence * Decision tree * Machine learning * Random forest * Regression
