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.
Multiple Sensor Monitoring in Drilling of CFRP/CFRP Stacks for Cognitive Tool Wear Prediction and Product Quality Assessment / Caggiano, Alessandra; Centobelli, Piera; Nele, Luigi; Teti, Roberto. - 62:(2017), pp. 3-8. (Intervento presentato al convegno 10th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME 2016 nel 2016) [10.1016/j.procir.2017.03.047].
Multiple Sensor Monitoring in Drilling of CFRP/CFRP Stacks for Cognitive Tool Wear Prediction and Product Quality Assessment
CAGGIANO, ALESSANDRA;CENTOBELLI, PIERA;NELE, LUIGI;TETI, ROBERTO
2017
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
