A multiple sensor monitoring procedure is developed with the aim to perform tool wear forecast in drilling of CFRP/CFRP stacks. Experimental drilling tests with a traditional twist drill bit and an innovative step drill bit are carried out using a multi-sensor system to acquire thrust force and torque signals during the process. The tool wear curve for each drill bit under different drilling conditions is obtained by measuring the tool flank wear. An artificial neural network for pattern recognition is developed to find correlations between selected sensor signal features and tool wear state, with the aim to forecast the tool wear values during drilling based on the information extracted from the acquired sensor signals.

Multiple sensor monitoring for tool wear forecast in drilling of CFRP/CFRP stacks with traditional and innovative drill bits / Caggiano, Alessandra; Napolitano, Francesco; Nele, Luigi; Teti, Roberto. - 67(2018), pp. 404-409. (Intervento presentato al convegno 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME ’17 tenutosi a Ischia (NA) nel 19-21 Luglio 2017) [10.1016/j.procir.2017.12.233].

Multiple sensor monitoring for tool wear forecast in drilling of CFRP/CFRP stacks with traditional and innovative drill bits

Alessandra Caggiano;Luigi Nele;Roberto Teti
2018

Abstract

A multiple sensor monitoring procedure is developed with the aim to perform tool wear forecast in drilling of CFRP/CFRP stacks. Experimental drilling tests with a traditional twist drill bit and an innovative step drill bit are carried out using a multi-sensor system to acquire thrust force and torque signals during the process. The tool wear curve for each drill bit under different drilling conditions is obtained by measuring the tool flank wear. An artificial neural network for pattern recognition is developed to find correlations between selected sensor signal features and tool wear state, with the aim to forecast the tool wear values during drilling based on the information extracted from the acquired sensor signals.
2018
Multiple sensor monitoring for tool wear forecast in drilling of CFRP/CFRP stacks with traditional and innovative drill bits / Caggiano, Alessandra; Napolitano, Francesco; Nele, Luigi; Teti, Roberto. - 67(2018), pp. 404-409. (Intervento presentato al convegno 11th CIRP Conference on Intelligent Computation in Manufacturing Engineering - CIRP ICME ’17 tenutosi a Ischia (NA) nel 19-21 Luglio 2017) [10.1016/j.procir.2017.12.233].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/705567
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