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July 8, 2026MachinesOpen Access

AI-Driven Prediction of Surface Roughness and Cutting Force in Milling Aluminum Alloy Under Data-Scarce Conditions

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Authors

MEMohammad Hossein EbrahimiShahroud University of Medical SciencesSNSeyed Ali NiknamWestern New England University

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Implication

Randomized trial develops AI models to predict surface roughness and cutting force in aluminum milling, suggesting new solutions for quality control.

Key Points

  • This study aims to improve predictions of surface roughness and cutting forces during aluminum milling using AI, especially when limited data is available.
  • Conducted 216 milling experiments on aluminum alloys AA2024-T351 and AA6061-T6.
  • Developed and optimized eight machine learning algorithms using a preprocessing strategy and feature engineering.
  • Evaluated model performance using R2, MAE, and RMSE metrics on a train–validation–test split.
  • XGBoost achieved the highest accuracy for surface roughness with R2 = 0.99829 and cutting force with R2 = 0.997.
  • Feed rate was identified as the most important parameter, accounting for 87.7% of importance in predicting surface roughness.
  • NGBoost provided uncertainty estimates, enhancing the interpretability of predictions.

Cite This Study

Ebrahimi et al. (2026) studied this question.

synapsesocial.com/papers/6a4de9add2ea289ef6283c5dhttps://doi.org/10.3390/machines14070756
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Also Consider

Synapse has enriched one closely related paper. Consider it for comparative context:

  1. 1Artificial Intelligence‐Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy2021 · 78 citations