Surface roughness prediction model in high-speed dry milling CFRP considering carbon fiber distribution

The surface roughness of carbon fiber-reinforced polymer (CFRP) components is extremely important because of the increasing demand for higher performance, reliability, and longer lifetime in aerospace and other manufacturing industries. However, the cutting mechanisms of CFRP are still unclear, which limits its formation mechanism prediction for surface roughness. Thus far, this study presents three action mechanisms in the CFRP machining process: accumulation bouncing on the matrix, impact effect on carbon fibers, and workpiece selfaction. The workpiece self-action was reflected and calculated using the light rope model, which is new in CFRP machining. Furthermore, the formation mechanisms of surface roughness were first elucidated in the highspeed dry (HSD) milling of CFRP. An accurate surface roughness prediction model was theoretically formulated considering the kinematics, dynamics, and carbon fiber distribution. Surface roughness was expressed as the three-dimensional arithmetic mean height. The surface roughness prediction model was established and confirmed to have a high prediction accuracy of 90.05%, demonstrating that the distribution of carbon fibers was the main influencing factor of the surface roughness. Moreover, nonlinear regression analysis was used to clarify the effects of cutting parameters on the surface roughness, impact effect, and relationship between the surface roughness and impact effect. The study verified the feasibility of HSD milling CFRP, and it also provided guidance for breaking the low-speed machining limits by improving the machining process.

Automated summary

This study investigates the cutting mechanisms and formation mechanisms of surface roughness in carbon fiber-reinforced polymer (CFRP) machining. The workpiece self-action is calculated using the light rope model, and the formation mechanisms of surface roughness in high-speed dry milling of CFRP are elucidated. An accurate surface roughness prediction model is established. Furthermore, the effects of cutting parameters on surface roughness and impact effect are clarified through nonlinear regression analysis.