Predictive Models for Mechanistic Cutting Force Coefficients and Surface Roughness in Carbon Fiber Reinforced Polymers Milling with Tool Wear

Machining carbon fiber composite materials poses significant challenges including rapid tool wear and decreased productivity. This research aims to address these challenges by developing a predictive model for surface roughness in carbon fiber composite milling operations using a cutting force-based approach.

This research applies a mechanistic cutting force model to force prediction in carbon fiber composite milling. Because tool wear can be significant in this application, the force model coefficients are defined as a function of the volume of material removed. This enables the cutting force growth with tool wear to be embedded within the model. Examples are provided for different tool coatings to demonstrate their importance, including carbide tools coated with AlTiCrN and polycrystalline diamond (PCD). Tool wear tests are completed where cutting forces are measured periodically during the milling process. Tool wear is investigated in terms of flank wear width (FWW). Additionally, the investigation includes the measurement of surface roughness on the workpiece with a 3D optical measurement system.

The data demonstrates a clear correlation between tool wear, cutting forces, and surface roughness. A model is proposed, leveraging the observed dependency of cutting forces on the volume of material removed to predict surface roughness during milling operations. The model provides a practical tool for machinists and engineers to anticipate surface quality variations based on real-time cutting force measurements, thereby enhancing productivity. This research contributes to the advancement of machining strategies for carbon fiber reinforced polymers by offering a predictive framework that facilitates improved surface quality control and efficiency in milling operations.