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<p>This study presents a machine learning framework for predicting the Young’s Modulus (YM) of biomedical titanium alloys to address stress shielding in implant applications. A Deep Neural Network (DNN) was developed using nineteen compositional features and physically meaningful descriptors. Prior to model training, the dataset was preprocessed using Box–Cox and Yeo–Johnson transformations to improve data distribution while preserving all samples. The model architecture incorporates multiple hidden layers with L1/L2 regularization and dropout to enhance generalization. Training was conducted using early stopping, terminating at 585 epochs to prevent overfitting. The optimized model achieved a testing Mean Squared Error (MSE) of 0.294 Gpa and r<sup>2</sup>-score of 0.82, demonstrating predictive performance. Comparative analysis with XGBoost, Random Forest, Gradient Boosting, and Support Vector Machine confirmed the capability of the proposed DNN model for capturing complex behavior non-linear relationships in Ti-Alloys data.</p>

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Predicting the Young’s Modulus of biomedical titanium alloys using machine learning: a data-driven approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Muhammad Shahmir Saif,
  • Muhammad Ali Siddiqui,
  • Fahim Raees

摘要

This study presents a machine learning framework for predicting the Young’s Modulus (YM) of biomedical titanium alloys to address stress shielding in implant applications. A Deep Neural Network (DNN) was developed using nineteen compositional features and physically meaningful descriptors. Prior to model training, the dataset was preprocessed using Box–Cox and Yeo–Johnson transformations to improve data distribution while preserving all samples. The model architecture incorporates multiple hidden layers with L1/L2 regularization and dropout to enhance generalization. Training was conducted using early stopping, terminating at 585 epochs to prevent overfitting. The optimized model achieved a testing Mean Squared Error (MSE) of 0.294 Gpa and r2-score of 0.82, demonstrating predictive performance. Comparative analysis with XGBoost, Random Forest, Gradient Boosting, and Support Vector Machine confirmed the capability of the proposed DNN model for capturing complex behavior non-linear relationships in Ti-Alloys data.