Edge trimming is a necessary machining operation to achieve the required dimension and surface quality of carbon fiber reinforced polymer (CFRP) components. Rapid tool wear occurs during the edge trimming process owing to the high strength and abrasive nature of the carbon fibers, leading to potential surface damage. To satisfy the production efficiency and part quality requirements, prediction and monitoring of tool wear progression are essential in edge trimming of CFRP.

This thesis presents the tool wear prediction and monitoring in edge trimming of unidirectional (UD) and multidirectional (MD) CFRPs using the artificial neural network (ANN) and random forest (RF) algorithms. The objective is to achieve the tool wear prediction and monitoring under various process conditions with limited training data. For edge trimming of UD CFRP, the feature parameters of the machine learning models consist of the instantaneous process parameters, such as the instantaneous radial force, fiber cutting angle, and uncut chip thickness. As a result, the training data obtained at one radial and axial depth of cut are applicable for tool wear prediction at other radial cutting depths. The experimental data shows a high correlation between the instantaneous radial force and the tool flank wear length. The proposed method achieves the tool wear length prediction within 10% error under various edge trimming conditions.

The experimental data from UD CFRP is used for tool wear monitoring in edge trimming of MD CFRP. The tool wear length ratios among the tool edge portions corresponding to different UD layers of the MD CFRP are experimentally identified. The overall radial force is predicted by individual force components from each UD layer, which are influenced by the cutting speed, feed rate, tool helix angle, and wear length distribution along the tool edge. The results show that the proposed model predicts the radial force in edge trimming of MD CFRP with different layer-up sequences within 20% error including the tool wear effect. The proposed method is able to perform tool wear monitoring in edge trimming of MD CFRP based on the force prediction with the predefined maximum tool wear length.