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<p>Significant attention was given to the use of machine learning (ML) in fused deposition modeling (FDM) to optimize printing processes. In this study, an ML-based framework was developed for establishing interpretable quantitative relationships between printing parameters and the mechanical properties of polylactic acid (PLA). A polynomial ElasticNet model was employed to derive closed-form predictive equations with a strong predictive performance (<i>R</i><sup>2</sup> <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>≈</mo> </math></EquationSource> </InlineEquation> 0.87 for ultimate tensile strength and <i>R</i><sup>2</sup> <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\approx\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>≈</mo> </math></EquationSource> </InlineEquation> 0.89 for elastic modulus). The layer height was identified as the dominant parameter. The proposed approach provides continuous, interpretable relationships to enable efficient process optimization in FDM.</p> Graphical abstract <p></p>

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Interpretable machine learning analysis of printing parameter effects on mechanical behavior of FDM-printed polylactic acid

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Mohammad Hossein Golbabaei,
  • Amin Kuhzadmohammadi,
  • Zachary Porter,
  • Hunter Garner,
  • Ning Zhang

摘要

Significant attention was given to the use of machine learning (ML) in fused deposition modeling (FDM) to optimize printing processes. In this study, an ML-based framework was developed for establishing interpretable quantitative relationships between printing parameters and the mechanical properties of polylactic acid (PLA). A polynomial ElasticNet model was employed to derive closed-form predictive equations with a strong predictive performance (R2 \(\approx\) ≈ 0.87 for ultimate tensile strength and R2 \(\approx\) ≈ 0.89 for elastic modulus). The layer height was identified as the dominant parameter. The proposed approach provides continuous, interpretable relationships to enable efficient process optimization in FDM.

Graphical abstract