Wear Prediction of Functionally Graded Composites Using Machine Learning
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View abstract on PubMed
Summary
This summary is machine-generated.Functionally graded composites from magnesium waste and eggshells show improved hardness and wear resistance. Machine learning models, particularly LightGBM, accurately predict wear behavior, reducing the need for extensive testing.
Area Of Science
- Materials Science and Engineering
- Composite Materials
- Tribology
Background
- Magnesium matrix composites offer potential for lightweight applications.
- Utilizing waste materials like magnesium chips and eggshells addresses environmental concerns and reduces costs.
- Functionally graded materials (FGMs) are desirable for applications requiring tailored properties across a component.
Purpose Of The Study
- To produce functionally graded composites using recycled magnesium and eggshell reinforcements via centrifugal casting.
- To investigate the wear behavior of these composites under varying loads, speeds, and distances.
- To develop and compare machine learning models for predicting the wear rate of these FGMs.
Main Methods
- Centrifugal casting was employed to create functionally graded composites with eggshell particles in a magnesium matrix.
- Wear tests were conducted across a range of loads, sliding speeds, and distances.
- Four machine learning algorithms were trained and evaluated on experimental data to predict wear rate.
Main Results
- Eggshell particles were successfully integrated in graded levels, increasing hardness by 25.26% and wear resistance by 19.8% in the outer zones compared to inner zones.
- Tree-based machine learning models outperformed deep neural networks in predicting wear rate.
- The LightGBM model demonstrated the highest accuracy in predicting wear rate across different zones.
Conclusions
- Recycled magnesium waste chips and eggshell particles can be effectively used to create cost-effective, functionally graded composites.
- The developed machine learning models offer an efficient method for evaluating FGM wear behavior, minimizing the need for extensive physical testing.
- These FGMs are suitable for automotive components requiring specific hardness and wear resistance profiles, promoting sustainable material utilization.
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