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Interpretable Machine Learning-Based Influence Factor Identification for 3D Printing Process-Structure Linkages.

Fuguo Liu1,2, Ziru Chen3, Jun Xu4

  • 1School of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi 830012, China.

Polymers
|September 28, 2024
PubMed
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This summary is machine-generated.

Optimizing 3D printing parameters using machine learning revealed that extrusion expansion ratio, elastic modulus, and elongation at break significantly impact print quality. This study clarifies key factors for achieving superior 3D printing effects.

Area of Science:

  • Materials Science and Engineering
  • Manufacturing Technology
  • Computational Science

Background:

  • Three-dimensional printing (3D printing) is a vital rapid prototyping technology in manufacturing.
  • Optimizing 3D printing parameters is crucial for achieving desired printing effects.
  • Understanding the influence of these parameters requires theoretical and data-driven approaches.

Purpose of the Study:

  • To predict the impact of 3D printing parameters on printing effects using machine learning.
  • To provide theoretical explanations for parameter influence using mathematical models.
  • To validate the importance of key parameters based on prior experience.

Main Methods:

  • Employed four machine learning algorithms: Support Vector Regression (SVR), Random Forest, Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGB).
Keywords:
SHAP valueSVRintegrated learninginterpretive machine learningthree-dimensional printing

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  • Utilized feature importance and SHAP (Shapley additive explanation) values to assess parameter influence.
  • Optimized hyperparameters using Bayesian optimization and grid search, followed by predictive modeling on a divided dataset.

    • Main Results:

    • Identified extrusion expansion ratio, elastic modulus, and elongation at break as the most influential parameters on the printing effect.
    • Compared the predictive performance of SVR, Random Forest, GBDT, and XGB models.
    • Confirmed findings align with established practical experience in 3D printing.

    Conclusions:

    • Machine learning effectively predicts the impact of 3D printing parameters on print outcomes.
    • Key material properties like extrusion expansion ratio, elastic modulus, and elongation at break are critical for print quality.
    • Future work will focus on further optimization and applying interpretable machine learning to enhance 3D printing efficiency and reliability.