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<p>The mechanical characteristics of carbon fibre-reinforced acrylonitrile butadiene styrene (ABS) composites specimen made via fused filament fabrication are examined in this work. The study centres on the optimization of three crucial process parameters, namely print orientation, raster width, and infill pattern, with the aim of augmenting the tensile, flexural, and compressive strengths of components that are 3D-printed. To increase mechanical performance, 15% carbon fibre reinforcement was added to ABS filaments. Nine parameter combinations were tried using Taguchi’s L9 orthogonal array, and the outcomes were examined using analysis of variance. The best compressive strength was attained using <i>XY</i> orientation, honeycomb infill pattern, and a 0.5&#xa0;mm raster width, while the ideal parameters for tensile and flexural strengths were determined to be <i>YZ</i> print orientation, concentric infill pattern, and a raster width of 0.3&#xa0;mm. Furthermore, to anticipate the mechanical properties, an artificial intelligence-based model called PSO-FIS—a combination of particle swarm optimization and fuzzy inference system—was created. It showed excellent accuracy and minimal prediction errors. The results demonstrate the potential of carbon fibre-reinforced ABS to produce 3D-printed parts that are stronger and more resilient, with applications in sectors like automotive and aerospace where optimal mechanical performance is essential.</p>

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Optimization of Mechanical Properties in Carbon Fiber-Reinforced ABS Composites Fabricated via Fused Filament Fabrication: An Experimental and AI-Based Predictive Approach

  • 期刊论文

  • 发表日期：

    2025年5月6日

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  • Chitta Ranjan Tripathy,
  • Arvind Katyayn,
  • Rajiv Kumar Sharma,
  • Nishant Kumar Chaudhary

摘要

The mechanical characteristics of carbon fibre-reinforced acrylonitrile butadiene styrene (ABS) composites specimen made via fused filament fabrication are examined in this work. The study centres on the optimization of three crucial process parameters, namely print orientation, raster width, and infill pattern, with the aim of augmenting the tensile, flexural, and compressive strengths of components that are 3D-printed. To increase mechanical performance, 15% carbon fibre reinforcement was added to ABS filaments. Nine parameter combinations were tried using Taguchi’s L9 orthogonal array, and the outcomes were examined using analysis of variance. The best compressive strength was attained using XY orientation, honeycomb infill pattern, and a 0.5 mm raster width, while the ideal parameters for tensile and flexural strengths were determined to be YZ print orientation, concentric infill pattern, and a raster width of 0.3 mm. Furthermore, to anticipate the mechanical properties, an artificial intelligence-based model called PSO-FIS—a combination of particle swarm optimization and fuzzy inference system—was created. It showed excellent accuracy and minimal prediction errors. The results demonstrate the potential of carbon fibre-reinforced ABS to produce 3D-printed parts that are stronger and more resilient, with applications in sectors like automotive and aerospace where optimal mechanical performance is essential.