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<p>This study focuses on the optimization of process parameters in vat photopolymerization-based LCD 3D printing to improve mechanical and surface properties. Critical parameters including layer thickness, exposure time, and printing orientation were systematically varied, and test specimens were fabricated and evaluated according to ASTM standards. Mechanical responses, including tensile strength and Young’s modulus, as well as surface roughness, were analyzed. A Regression-Driven Parameter Optimization (RDPO) model employing multiple linear and polynomial regression was developed using the experimental dataset to establish predictive relationships between process parameters and output responses. The model was validated through independent trials, showing strong consistency between predicted and experimental values. The optimization results revealed that a layer thickness of 0.035&#xa0;mm, exposure time of 4.0&#xa0;s, and print orientation of 8.0° produced improved tensile strength, elastic modulus, and surface finish. These findings demonstrate the robustness of the RDPO model in accurately predicting and optimizing process outcomes, offering a reliable pathway for achieving enhanced performance in LCD-based vat photopolymerization.</p>

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Regression-Driven Optimization of Vat Photopolymerization-Based 3D Printing Parameters for Improved Mechanical Properties

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Deepak Kumar,
  • Ranjan Jha

摘要

This study focuses on the optimization of process parameters in vat photopolymerization-based LCD 3D printing to improve mechanical and surface properties. Critical parameters including layer thickness, exposure time, and printing orientation were systematically varied, and test specimens were fabricated and evaluated according to ASTM standards. Mechanical responses, including tensile strength and Young’s modulus, as well as surface roughness, were analyzed. A Regression-Driven Parameter Optimization (RDPO) model employing multiple linear and polynomial regression was developed using the experimental dataset to establish predictive relationships between process parameters and output responses. The model was validated through independent trials, showing strong consistency between predicted and experimental values. The optimization results revealed that a layer thickness of 0.035 mm, exposure time of 4.0 s, and print orientation of 8.0° produced improved tensile strength, elastic modulus, and surface finish. These findings demonstrate the robustness of the RDPO model in accurately predicting and optimizing process outcomes, offering a reliable pathway for achieving enhanced performance in LCD-based vat photopolymerization.