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<p>Natural rubber (NR), valued for its high toughness and elongation, was incorporated into digital light processing (DLP) 3D printing resins to enhance mechanical performance using epoxidised Philippine NR (EPNR), optimised by statistical and machine learning methods. EPNR blends (0–10%) with cationic photo-initiator (CPI) and photo-curable commercial resin (PCR) were formulated using a solvent blending technique. Fourier transform infrared spectroscopy and simultaneous differential scanning calorimetry-thermogravimetry have confirmed successful epoxidation and improve thermal stability. The optimal formulation (6.36% EPNR, 2.10% CPI, and 91.54% PCR) achieved a toughness of 16,406.8&#xa0;J/m³ and an elongation at break of 39.41%, marking a 78.1% improvement over unmodified PCR. Mixture design (MD) and artificial neural network (ANN) modelling yielded high predictive accuracy (R² ∼0.986), with ANN outperforming MD in minimising error. Guided by the trained ANN model, 3D printed structures exhibited improved chemical resistance and thermal stability, rivalling commercial resins. This work highlights the potential of EPNR as a reinforcement additive to enhance DLP resin performance, paving the way for innovative applications in additive manufacturing.</p>

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Epoxidised Philippine natural rubber for tough and versatile 3D printable resins: a mixture design and neural network approach

  • 期刊论文

  • 发表日期：

    2025年5月20日

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  • Roland Oliver A. Calabia,
  • Joseph Emmanuel D. Gomez,
  • Ian M. Lasala,
  • Carlos Miguel A. Ligsay,
  • Reymark D. Maalihan,
  • Anita P. Aquino,
  • Reygan H. Sangalang

摘要

Natural rubber (NR), valued for its high toughness and elongation, was incorporated into digital light processing (DLP) 3D printing resins to enhance mechanical performance using epoxidised Philippine NR (EPNR), optimised by statistical and machine learning methods. EPNR blends (0–10%) with cationic photo-initiator (CPI) and photo-curable commercial resin (PCR) were formulated using a solvent blending technique. Fourier transform infrared spectroscopy and simultaneous differential scanning calorimetry-thermogravimetry have confirmed successful epoxidation and improve thermal stability. The optimal formulation (6.36% EPNR, 2.10% CPI, and 91.54% PCR) achieved a toughness of 16,406.8 J/m³ and an elongation at break of 39.41%, marking a 78.1% improvement over unmodified PCR. Mixture design (MD) and artificial neural network (ANN) modelling yielded high predictive accuracy (R² ∼0.986), with ANN outperforming MD in minimising error. Guided by the trained ANN model, 3D printed structures exhibited improved chemical resistance and thermal stability, rivalling commercial resins. This work highlights the potential of EPNR as a reinforcement additive to enhance DLP resin performance, paving the way for innovative applications in additive manufacturing.