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<p>In order to improve the performance of Carbon Fiber-Reinforced Polymer (CFRP), the surface modifications, the chemical and plasma treated fiber have been prepared. On varying the laser factors, power, velocity and pressure, the Dimensional Deviation (DD), Heat Affected Zone width (HAZ width) and Residual Stress (RS) development in HAZ are studied under the untreated, chemical, plasma treated fiber surface. The L16 orthogonal array, analysis of variance, phase analysis and Gaussian Process Regression (GPR) model are employed.&#xa0;Plasma-treated surfaces exhibited the 4.4% DD, 93&#xa0;µm HAZ width and 150&#xa0;MPa RS compared to untreated and chemical treated surface. ANOVA results indicated velocity as the most significant parameter (<i>p</i> = 0.013), followed by power (<i>p</i> = 0.058). X-ray diffraction (XRD) analysis showed C, O, N, and Ar peaks, confirming surface modification. The Gaussian Process Regression (GPR) model demonstrated superior predictive accuracy.</p>

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Study on Triangular Through-Hole Characteristics Using Laser Process Under Untreated, Chemical and Plasma Treated CFRP

  • 期刊论文

  • 发表日期：

    2026年1月15日

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  • V. Senthil Kannan,
  • K. Lenin,
  • B. Saravanan,
  • D. Raj Kumar

摘要

In order to improve the performance of Carbon Fiber-Reinforced Polymer (CFRP), the surface modifications, the chemical and plasma treated fiber have been prepared. On varying the laser factors, power, velocity and pressure, the Dimensional Deviation (DD), Heat Affected Zone width (HAZ width) and Residual Stress (RS) development in HAZ are studied under the untreated, chemical, plasma treated fiber surface. The L16 orthogonal array, analysis of variance, phase analysis and Gaussian Process Regression (GPR) model are employed. Plasma-treated surfaces exhibited the 4.4% DD, 93 µm HAZ width and 150 MPa RS compared to untreated and chemical treated surface. ANOVA results indicated velocity as the most significant parameter (p = 0.013), followed by power (p = 0.058). X-ray diffraction (XRD) analysis showed C, O, N, and Ar peaks, confirming surface modification. The Gaussian Process Regression (GPR) model demonstrated superior predictive accuracy.