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Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength
https://orcid.org/http://orcid.org/0000-0001-7717-8710
In the present work, machine learning algorithms were used to estimate the fatigue strength of cast aluminium alloy. The dataset of 39 alloys of cast aluminium alloys was taken for developing the models. Four machine learning algorithms Linear Regression, Support Vector Machine, Artificial Neural Network and Random Forest was taken for model development. These models were developed by using Python programming language and then evaluation is made by using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Coefficient of Correlation (R).With each model, fatigue strength was predicted. It has been found that MAPE has the least value for ANN which indicates that, ANN is best suitable model for prediction of fatigue strength. The ANN model is validated with available results which are close agreement with predicted values. Along with ANN, Linear Regression model shows better results after evaluating by RMSE and R evaluation techniques.
Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength
https://orcid.org/http://orcid.org/0000-0001-7717-8710
In the present work, machine learning algorithms were used to estimate the fatigue strength of cast aluminium alloy. The dataset of 39 alloys of cast aluminium alloys was taken for developing the models. Four machine learning algorithms Linear Regression, Support Vector Machine, Artificial Neural Network and Random Forest was taken for model development. These models were developed by using Python programming language and then evaluation is made by using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Coefficient of Correlation (R).With each model, fatigue strength was predicted. It has been found that MAPE has the least value for ANN which indicates that, ANN is best suitable model for prediction of fatigue strength. The ANN model is validated with available results which are close agreement with predicted values. Along with ANN, Linear Regression model shows better results after evaluating by RMSE and R evaluation techniques.
Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength
https://orcid.org/http://orcid.org/0000-0001-7717-8710
In the present work, machine learning algorithms were used to estimate the fatigue strength of cast aluminium alloy. The dataset of 39 alloys of cast aluminium alloys was taken for developing the models. Four machine learning algorithms Linear Regression, Support Vector Machine, Artificial Neural Network and Random Forest was taken for model development. These models were developed by using Python programming language and then evaluation is made by using root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Coefficient of Correlation (R).With each model, fatigue strength was predicted. It has been found that MAPE has the least value for ANN which indicates that, ANN is best suitable model for prediction of fatigue strength. The ANN model is validated with available results which are close agreement with predicted values. Along with ANN, Linear Regression model shows better results after evaluating by RMSE and R evaluation techniques.
Analysis of Various Machine Learning Algorithms for Cast Aluminium Alloy to Estimate Fatigue Strength
J. Inst. Eng. India Ser. D
Utpat, Vedant Shrikant (Autor:in) / Kulkarni, Swanand Gajanan (Autor:in)
01.06.2023
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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