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<p>This study investigates the effect of graphene-reinforced polylactic acid (GR-PLA) nanocomposites and key FDM factors on wear and fatigue properties of fabricated specimens. After mechanically blending GR-PLA, the nanocomposite filament was prepared using a 3Devo Filament Maker. Specimens were then fabricated using a Snapmaker 2.0 A350 FDM 3D Printer, as per ASTM G99 and D7791 standards for wear and fatigue testing, respectively. A Pin-on-Disc Apparatus and the Nano-Plug and Play from Bliss were utilized to evaluate the wear and fatigue behavior, assessing the impact of graphene reinforcement on the composite’s strength and durability. Based on the experimental central composite design (CCD) matrix generated using Design-Expert Software, the minimum wear rate of 0.0211&#xa0;mm<sup>3</sup>/m was observed at 4% GR concentration, 0.1&#xa0;mm layer height, 80% infill density, and 237.5&#xa0;°C printing temperature. The maximum fatigue strength of 10&#xa0;MPa was achieved at 4% GR concentration, 0.15&#xa0;mm layer height, 80% infill density, and 252.5&#xa0;°C printing temperature. Field Emission Scanning Electron Microscopy (FE-SEM) examined the dispersion quality and interfacial adhesion within the PLA matrix. A hybrid multi-objective genetic algorithm artificial neural network (MOGA-ANN) approach was developed to optimize the FDM process parameters for enhanced mechanical performance of GR-PLA. An ANN model was initially trained and validated using experimental results to predict this performance. The MOGA then further optimized these predictions. The resulting optimal parameters (4.99% GR concentration, 0.13&#xa0;mm layer height, 91.92% infill density, and 242.35&#xa0;°C printing temperature) yielded the lowest wear rate of 0.0188&#xa0;mm<sup>3</sup>/m and the highest fatigue strength of 13.24&#xa0;MPa, which were experimentally validated.</p>

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Investigation of mechanical and morphological properties of 3D-printed GR-PLA nanocomposites with process parameter optimization using MOGA-ANN

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Akash Ahlawat,
  • Ashish Phogat,
  • Upender Punia,
  • Watan Pal,
  • Ravinder Kumar Sahdev,
  • Deepak Chhabra

摘要

This study investigates the effect of graphene-reinforced polylactic acid (GR-PLA) nanocomposites and key FDM factors on wear and fatigue properties of fabricated specimens. After mechanically blending GR-PLA, the nanocomposite filament was prepared using a 3Devo Filament Maker. Specimens were then fabricated using a Snapmaker 2.0 A350 FDM 3D Printer, as per ASTM G99 and D7791 standards for wear and fatigue testing, respectively. A Pin-on-Disc Apparatus and the Nano-Plug and Play from Bliss were utilized to evaluate the wear and fatigue behavior, assessing the impact of graphene reinforcement on the composite’s strength and durability. Based on the experimental central composite design (CCD) matrix generated using Design-Expert Software, the minimum wear rate of 0.0211 mm3/m was observed at 4% GR concentration, 0.1 mm layer height, 80% infill density, and 237.5 °C printing temperature. The maximum fatigue strength of 10 MPa was achieved at 4% GR concentration, 0.15 mm layer height, 80% infill density, and 252.5 °C printing temperature. Field Emission Scanning Electron Microscopy (FE-SEM) examined the dispersion quality and interfacial adhesion within the PLA matrix. A hybrid multi-objective genetic algorithm artificial neural network (MOGA-ANN) approach was developed to optimize the FDM process parameters for enhanced mechanical performance of GR-PLA. An ANN model was initially trained and validated using experimental results to predict this performance. The MOGA then further optimized these predictions. The resulting optimal parameters (4.99% GR concentration, 0.13 mm layer height, 91.92% infill density, and 242.35 °C printing temperature) yielded the lowest wear rate of 0.0188 mm3/m and the highest fatigue strength of 13.24 MPa, which were experimentally validated.