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<p>The qualification of additive manufacturing processes is necessary to ensure print quality and material properties, but it is cost intensive. To predict suitable process parameters, this study compares classical statistical methods of experimental design with modern machine learning (ML) methods. The vat polymerisation process with a highly filled resin is investigated through combining data collection (response surface methodology, Sobol, particle swarm optimisation) and regression methods (polynomial regression, various ML algorithms). The results show that ML methods deliver more robust models on average than classical regression, although no universally optimal combination could be identified. Individual combinations such as particle swarm optimisation and support vector regression, CatBoost and random forest delivered the best results, while polynomial regression showed major shortcomings with the available data. For a higher tensile strength, high exposure intensity, short washing time and long tempering time proved to be decisive parameters.</p>

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Sampling techniques for experimental design in additive manufacturing: testing the accuracy of machine learning models through data collection methods to improve the mechanical properties of vat polymerisation resins

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Valentin Wiesner,
  • Jakob Hornung,
  • Frank Deinzer,
  • Alexander Rost,
  • Markus Stark,
  • Marcel Trier

摘要

The qualification of additive manufacturing processes is necessary to ensure print quality and material properties, but it is cost intensive. To predict suitable process parameters, this study compares classical statistical methods of experimental design with modern machine learning (ML) methods. The vat polymerisation process with a highly filled resin is investigated through combining data collection (response surface methodology, Sobol, particle swarm optimisation) and regression methods (polynomial regression, various ML algorithms). The results show that ML methods deliver more robust models on average than classical regression, although no universally optimal combination could be identified. Individual combinations such as particle swarm optimisation and support vector regression, CatBoost and random forest delivered the best results, while polynomial regression showed major shortcomings with the available data. For a higher tensile strength, high exposure intensity, short washing time and long tempering time proved to be decisive parameters.