In this study, a comprehensive investigation was undertaken to analyze the impact of various factors on thrust force and burr damage in carbon fiber reinforced polymer laminates during drilling operations. The factors examined included the incorporation of nano-graphene, the application of ultrasonic vibration, the type of cutting tool, and the feed rate. Statistical and machine learning techniques were employed to analyze the data, and image processing was utilized to illustrate the extent of burr damage surrounding the drilled holes. The developed model exhibited satisfactory prediction accuracy with minimal error rates. Statistical analysis revealed that the feed rate exerted the greatest influence on thrust force and facilitated the burr generation. Likewise, the addition of nano-graphene resulted in an increased thrust force due to improved rupture limits, consequently leading to cleaner holes with minimal burr damage. Furthermore, the implementation of ultrasonic vibration and the use of high-cobalt cutting tools were found to enhance drilling performance by reducing thrust force and minimizing the burr formation. The best hole quality was achieved at the lowest feed rate, in combination with a cutting tool containing 8% cobalt and the utilization of ultrasonic vibration.
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