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Updated: Jun 27, 2026

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition
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Explainable Ensemble Machine Learning for Predicting Deposition Characteristics in Advanced Additive Manufacturing.

Sandeep Jain1, Pradyumn Kumar Arya2

  • 1School of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

Micromachines
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

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Gradient Boosting (GB) and XGBoost (XGB) models accurately predict deposition behavior in advanced manufacturing. These machine learning models offer a reliable framework for optimizing complex process parameters, enhancing efficiency and accuracy.

Area of Science:

  • Advanced Manufacturing
  • Materials Science
  • Machine Learning

Background:

  • Precise prediction of deposition behavior is critical for optimizing advanced manufacturing processes.
  • Key deposition responses include bead width, bead height, energy input, and volumetric input.
  • Process parameters like laser power, travel speed, and wire feed rate influence deposition outcomes.

Purpose of the Study:

  • To develop and evaluate seven machine learning models for predicting deposition responses.
  • To identify the most accurate and computationally efficient model for process parameter optimization.
  • To interpret the influence of input parameters on deposition behavior using SHAP analysis.

Main Methods:

  • Seven machine learning models were developed: Random Forest, Gradient Boosting, XGBoost, LightGBM, Extra Trees, Support Vector Regression, and Elastic Net.
Keywords:
SHAP analysisadvanced additive manufacturingdeposition characteristicsensemble machine learningmachine learning in manufacturing

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Last Updated: Jun 27, 2026

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Published on: March 13, 2018

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Published on: March 28, 2025

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  • Model performance was assessed using Mean Absolute Error, Root Mean Square Error, and Coefficient of Determination.
  • SHAP analysis was employed to interpret feature importance and model predictions.

    • Main Results:

    • Ensemble models, particularly Gradient Boosting (GB) and XGBoost (XGB), outperformed traditional machine learning techniques.
    • The GB model demonstrated the best overall performance with high prediction accuracy and generalization.
    • Wire feed rate primarily influences volumetric deposition, while laser power and travel speed control energy input and bead geometry.

    Conclusions:

    • Ensemble boosting models provide a robust framework for understanding complex relationships in advanced manufacturing deposition processes.
    • The GB model is computationally efficient and suitable for real-world applications.
    • Accurate prediction of deposition behavior using machine learning facilitates process optimization and enhances manufacturing outcomes.