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Effect of laser ablation on mechanical performance of graphene-filled glass fibre reinforced polymer repaired composites

  • Santosh Kumar Muniyappa , Bharatish Achutarao ORCID logo EMAIL logo , Mamtha Venkatram , Gangadhar Angadi and Sivakumar Solaiachari
Published/Copyright: October 1, 2024
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Abstract

This paper aims to investigate the effect of laser ablation on the mechanical performance of graphene-filled glass fibre reinforced polymer (GFRP) repaired lap joints. The performance characteristics of repaired laminates were measured in terms of surface roughness, tensile strength and flexural strength according to ASTM standards. While the surface morphology was examined using a confocal microscope, scanning electron microscopy was adopted to analyse the laser ablated fibre–matrix interface. The average surface roughness significantly increased with an increase in laser power from 4 to 10 W which was attributed to the presence of graphene and burnt fibres. The laser ablation conditions corresponding to 10 W laser power, 300 mm s−1 scanning speed, 20 kHz of pulse frequency, 0.05 mm line spacing and 5 laser passes lead to the highest tensile strength (36.033 MPa) and bending strength (30.972 MPa) of GFRP laminates. The laser ablated microstructure was characterised by fibre pull-out, epoxy residue, burnt, clean fibres as evidenced by scanning electron microscopy.


Corresponding author: Bharatish Achutarao, Department of Mechanical Engineering, RV College of Engineering, RV Vidyaniketan Post 560059, Bengaluru, India, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: First author Santhosh Kumar performed laser ablation experiments on GFRP composites and collected data Dr Bharatish A fromulated the problem, analysed the data and supervised the research Dr Mamta V drafted the manuscript and plotted necessary graphs and assisted in the overall research Dr Gangadhar Angadi assisted inn preparing the composites and analysing the responses Dr Sivakumar Solaiachari assisted in conducting the experiments and revising the paper.

  3. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  4. Conflict of interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2023-10-18
Accepted: 2024-06-28
Published Online: 2024-10-01
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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