Synthèse
Volume 29, Numéro 2, Pages 25-35
2024-12-26
Machine Learning Prediction Of Cutting Parameters Of En-aw-1350 Aluminum Alloy Using Regression Models
Authors : Khrouf Fakhreddine .
Abstract
The paper proposes the use of machine learning models to predict the cutting properties when turning EN-AW-1350 aluminum alloy under dry cutting conditions. cutting speeds (m/min), feed rates (mm/rev) and depths of cut (mm) are the main input control parameters selected for the present investigation. The established models can predict the surface roughness values (Ra) during the aluminum hard turning operation, which can guide the direction of experiments and eliminate the need for time-consuming traditional experimental procedures. The prediction performance of six regression methods (Decision Tree (DT), linear regression (LR), eXtreme Gradient Boosting (XGBoost), AdaBoost, Support Vector Machine (SVM) and Random Forest (RF)) were evaluated with a test set/training set ratio of 8 to 2. Among the six regression methods, XGBoost had the best prediction effect on the surface roughness of EN-AW-1350 aluminum alloy with a low mean root mean square error (RMSE) and coefficient of determination (R2) close to 1.
Keywords
Aluminum alloy ; Machine learning ; Turning ; Surface roughness ; Regression methods
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