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<p>Elongation at fracture is one of the most sensitive yet difficult-to-predict mechanical properties in selective laser melting (SLM), due to complex and nonlinear interactions among process parameters and defect formation mechanisms. In this study, a curated dataset of more than 400 experimentally reported data points was systematically constructed from the literature, linking key SLM parameters—including laser power, scan speed, hatch distance, layer thickness, and spot size—to elongation. Statistical analysis revealed that individual parameters exhibit only weak linear correlations with elongation, highlighting the limitations of traditional regression-based approaches. To address this challenge, a unified machine learning framework was developed to benchmark seven predictive models spanning linear, kernel-based, distance-based, ensemble, and neural network paradigms. The results demonstrate that nonlinear and ensemble models significantly outperform linear approaches, with Gradient Boosting achieving the highest predictive accuracy (R² = 0.871), followed by Artificial Neural Networks (R² = 0.815) and Random Forest (R² = 0.699). In contrast, linear models explain less than 20% of the variance, confirming their inadequacy for ductility prediction. The key contribution of this work lies in providing a quantitative and systematic comparison of predictive strategies for elongation, and in demonstrating that process–property relationships in SLM cannot be reliably captured without advanced nonlinear modeling. These findings provide practical guidance for process optimization and establish a data-driven foundation for future integration of microstructural features in predictive frameworks.</p>

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From correlation to prediction: machine learning approaches to elongation behavior in SLM

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Yi-Jen Huang,
  • Amir Reza Ansari Dezfoli,
  • Li-Shang Lin,
  • Sanaz Hadidchi

摘要

Elongation at fracture is one of the most sensitive yet difficult-to-predict mechanical properties in selective laser melting (SLM), due to complex and nonlinear interactions among process parameters and defect formation mechanisms. In this study, a curated dataset of more than 400 experimentally reported data points was systematically constructed from the literature, linking key SLM parameters—including laser power, scan speed, hatch distance, layer thickness, and spot size—to elongation. Statistical analysis revealed that individual parameters exhibit only weak linear correlations with elongation, highlighting the limitations of traditional regression-based approaches. To address this challenge, a unified machine learning framework was developed to benchmark seven predictive models spanning linear, kernel-based, distance-based, ensemble, and neural network paradigms. The results demonstrate that nonlinear and ensemble models significantly outperform linear approaches, with Gradient Boosting achieving the highest predictive accuracy (R² = 0.871), followed by Artificial Neural Networks (R² = 0.815) and Random Forest (R² = 0.699). In contrast, linear models explain less than 20% of the variance, confirming their inadequacy for ductility prediction. The key contribution of this work lies in providing a quantitative and systematic comparison of predictive strategies for elongation, and in demonstrating that process–property relationships in SLM cannot be reliably captured without advanced nonlinear modeling. These findings provide practical guidance for process optimization and establish a data-driven foundation for future integration of microstructural features in predictive frameworks.