Quality monitoring in milling of unidirectional CFRP through wavelet packet transform of force signals

Abstract

Machining of CFRP is challenging and necessitates efficient and robust process monitoring techniques to minimize the machining induced damage such as fiber pullouts and delamination. In this study, wavelet packet transform of forces signals was used to monitor the surface quality of CFRP subjected to conventional edge trimming. Conventional milling experiments were performed on unidirectional CFRP machined at differ fiber orientation angles - 0°, 45°, 90° and 135°. The feed rate was varied between 0.025 and 0.75 mm/tooth. Depending on the fiber orientation, the ten point average roughness Rz varied between 2.9 and 104.1 µm. A novel algorithm using Wavelet Packet Decomposition was proposed to identify the signal features that could effectively establish a correlation between signal features, process variables (feed and speed) and surface roughness Rz. A bank of 35 different mother wavelets with decomposition levels up to 10 was explored. Seven different features were calculated for the wavelet packets obtained upon decomposition. Optimal wavelet parameters were identified based on the regression statistics. Among others, two features – standard deviation and energy-entropy coefficient were identified as primary candidates which resulted in roughness prediction with R2>91%. In addition, the morphology and removal mechanisms of the machined surfaces was examined using scanning electron microscopy. The nexus between those surfaces and signals was established which corroborated the utility of the proposed algorithm.