Utilization of ANN for the Prediction of Mechanical Properties in AlP0507-MWCNT-RHA Composites

Srivastava, Nitin; Singh, Lavish Kumar; Yadav, Manoj Kumar

doi:10.1007/s12540-023-01552-1

Met. Mater. Int.

2023

DOI: 10.1007/s12540-023-01552-1

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Utilization of ANN for the Prediction of Mechanical Properties in AlP0507-MWCNT-RHA Composites

Nitin Srivastava,

Lavish Kumar Singh,

Manoj Kumar Yadav

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2024

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Cited by 1 publication

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Aluminum Alloy Design by La Amount through Machine Learning and Experimental Verification

Kim,

Park,

Yang

et al. 2024

Korean J. Met. Mater.

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The development and design of metal materials have been carried out through experimental method and simulation based on theoretic. Recently, with the widespread application of artificial intelligence (AI) in various fields, many studies have been actively incorporating artificial intelligence into the field of metal material design. Especially, many studies have been reported on adding rare-earth elements to aluminum alloys to improve corrosion resistance and mechanical properties using AI. However, the performance evaluation of artificial intelligence through experimental verification has not yet been reported related to metal material. In this study, we investigated the artificial intelligence algorithm capable of predicting the hardness based on the composition ratio of aluminum alloy with added Lanthanum (La) using experimental data and conducted a comparative analysis of the predicted hardness values. The machine learning models employed Adaptive Boosting Regressor (ADA), Gradient Boosting Regressor (GBR), Random Forest Regressor (RF), and Extra Trees Regressor (ET). The dataset comprised 1,210 encompassing 9 composition elements constituting the alloy. In the result, the findings revealed that the ET model demonstrated the most effective performance in predicting hardness. In addition, the microstructure became fine and showed the highest hardness at 0.5 wt.% La and hardness tended to decrease as the amount of La increased. The ET model showed excellent performance in predicting this tendency through experimental verification.

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Aluminum Alloy Design by La Amount through Machine Learning and Experimental Verification

Kim,

Park,

Yang

et al. 2024

Korean J. Met. Mater.

View full text Add to dashboard Cite

show abstract

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Utilization of ANN for the Prediction of Mechanical Properties in AlP0507-MWCNT-RHA Composites

Cited by 1 publication

References 34 publications

Aluminum Alloy Design by La Amount through Machine Learning and Experimental Verification

Aluminum Alloy Design by La Amount through Machine Learning and Experimental Verification

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