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<p>This study presents a machine learning (ML)-based approach to predict surface roughness, R<sub>a</sub> during dry grinding of Ti-6Al-4&#xa0;V alloy using Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Linear Regression, and Polynomial Regression models. Experiments were conducted with Aluminium Oxide (Al₂O₃) and Silicon Carbide (SiC) wheels at varying feed rates (0.2–0.9&#xa0;mm/rev) and depths of cut (0.02–0.08&#xa0;mm). Results showed that Al₂O₃ consistently produced lower R<sub>a</sub> values.Increased feed and depth of cut led to rougher surfaces. XGBoost algorithm achieved the highest prediction accuracy (R² = 0.90), effectively capturing nonlinear dependencies. Feature importance analysis identified feed rate as the most influential factor (Importance Score &gt; 0.85), followed by depth of cut and wheel type. These findings demonstrate the potential of ML, particularly XGBoost, for optimizing grinding parameters and enhancing surface quality in Ti-6Al-4&#xa0;V machining.</p>

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Prediction of the surface roughness of Ti-6Al-4 V alloy during surface grinding using machine learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月1日

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  • K. Siri Varsha Reddy,
  • B. Adithi,
  • R. Nirupashree,
  • S. Hari Lokitha,
  • T. Satish Kumar,
  • Ranjan Kumar Ghadai,
  • Kanak Kalita

摘要

This study presents a machine learning (ML)-based approach to predict surface roughness, Ra during dry grinding of Ti-6Al-4 V alloy using Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Linear Regression, and Polynomial Regression models. Experiments were conducted with Aluminium Oxide (Al₂O₃) and Silicon Carbide (SiC) wheels at varying feed rates (0.2–0.9 mm/rev) and depths of cut (0.02–0.08 mm). Results showed that Al₂O₃ consistently produced lower Ra values.Increased feed and depth of cut led to rougher surfaces. XGBoost algorithm achieved the highest prediction accuracy (R² = 0.90), effectively capturing nonlinear dependencies. Feature importance analysis identified feed rate as the most influential factor (Importance Score > 0.85), followed by depth of cut and wheel type. These findings demonstrate the potential of ML, particularly XGBoost, for optimizing grinding parameters and enhancing surface quality in Ti-6Al-4 V machining.