Multiple Sensor Monitoring in Drilling of CFRP/CFRP Stacks for Cognitive Tool Wear Prediction and Product Quality Assessment

Drilling of stacks made of two overlaid carbon fiber reinforced plastic (CFRP) composite laminates for aeronautical assembly applications is investigated through multiple sensor monitoring and sensor fusion technology. An experimental testing campaign under different drilling conditions is carried out using a monitoring system endowed with thrust force and torque sensors. Advanced signal processing and analysis in the frequency domain are employed for feature extraction and selection to construct sensor fusion feature pattern vectors to be fed to artificial neural network based cognitive paradigms for the identification of correlations with tool wear development.