Predicting surface roughness in machining aluminum alloys taking into account material properties

ABSTRACT

This study investigates the use of machine learning models to predict surface roughness (Ra) in milling multi-grade aluminum alloys without prior knowledge of optimal cutting parameters. A diverse milling dataset encompassing material properties and cutting parameters from various aluminum alloy grades was compiled from research articles. Four machine learning algorithms, Extreme Gradient Boosting (XGB), Random Forest (RFR), Catalogical Gradient Boosting (CAT), and Gradient Boosting Regression (GBR), were employed to develop the predictive model. The dataset underwent cleaning, imputation, and outlier removal to ensure data quality. Feature engineering incorporated material properties and cutting parameters for model training. Performance metrics such as RMSE, MAPE, and R² were used to assess the models’ accuracy. The SHapley Additive exPlanations (SHAP) technique was employed to interpret the models and identify influential features. GBR achieved the highest prediction accuracy with an RMSE of 0.2507 µm, MAPE of 23.36%, and R² of 0.8709. Thermal conductivity, feed rate, and cutting speed were consistently identified as the most influential factors, although their rankings differed slightly. This study successfully developed a GBR model for effective Ra prediction in aluminum alloy milling, supporting advancements in smart manufacturing by enabling accurate surface quality prediction and data-driven process optimization through machine learning.

KEYWORDS:

• Surface roughness
• End milling
• Multi-grades aluminum alloys
• Machine learning
• Material properties

Disclosure statement

No potential conflict of interest was reported by the author(s)

Data availability

The raw/processed data required to reproduce these findings will be made available on request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/0951192X.2024.2372252.

Additional information

Funding

This research was funded by Phenikaa University under grant number PU2022-1-A-01.