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<p>This work introduces a unique method for adding titanium dioxide (TiO₂) and antimony-doped tin oxide (ATO) nanoparticles to 3D-printed acrylonitrile butadiene styrene (ABS) composites to improve their mechanical performance. Nanocomposites were fabricated using fused filament fabrication (FFF) with optimized 3D printing parameters. The mechanical performance was significantly improved, with tensile strength increasing by 42% (from 37.2 to 52.8&#xa0;MPa), flexural strength by 28.7%<b> (</b>from 58.6 to 75.4&#xa0;MPa), and microhardness by 43.2% (from 9.5 to 13.6&#xa0;HV) at an optimal nanoparticles concentration of 2.5&#xa0;wt%. To predict mechanical behavior, a Sparse Spectra Graph Convolutional Neural Network (SSGCNN) was developed and achieved a prediction accuracy of 99.2%, outperforming traditional models such as BOA, NSGA, and ANN. These results demonstrate the effectiveness of nanoparticles reinforcement and advanced AI modeling in developing high-strength, wear-resistant ABS composites suitable for aerospace, automotive, and biomedical applications.</p>

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Enhancing the structural integrity and load-bearing capacity of 3D-printed ABS polymer materials using innovative approach

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Ram,
  • Sandip Mane,
  • H. Shaheen,
  • Gurumurthy Bangalore Ramaiah

摘要

This work introduces a unique method for adding titanium dioxide (TiO₂) and antimony-doped tin oxide (ATO) nanoparticles to 3D-printed acrylonitrile butadiene styrene (ABS) composites to improve their mechanical performance. Nanocomposites were fabricated using fused filament fabrication (FFF) with optimized 3D printing parameters. The mechanical performance was significantly improved, with tensile strength increasing by 42% (from 37.2 to 52.8 MPa), flexural strength by 28.7% (from 58.6 to 75.4 MPa), and microhardness by 43.2% (from 9.5 to 13.6 HV) at an optimal nanoparticles concentration of 2.5 wt%. To predict mechanical behavior, a Sparse Spectra Graph Convolutional Neural Network (SSGCNN) was developed and achieved a prediction accuracy of 99.2%, outperforming traditional models such as BOA, NSGA, and ANN. These results demonstrate the effectiveness of nanoparticles reinforcement and advanced AI modeling in developing high-strength, wear-resistant ABS composites suitable for aerospace, automotive, and biomedical applications.