scholarly journals Tool Condition Monitoring of Single-point Dressing Operation by Digital Signal Processing of AE and AI

Procedia CIRP ◽  
2018 ◽  
Vol 67 ◽  
pp. 307-312 ◽  
Author(s):  
Doriana M. D’Addona ◽  
Salvatore Conte ◽  
Wenderson Nascimento Lopes ◽  
Paulo R. de Aguiar ◽  
Eduardo C. Bianchi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Condition Monitoring ◽  
Single Point ◽  
Digital Signal ◽  
Tool Condition Monitoring ◽  
Tool Condition

scholarly journals Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation

2017 ◽  
Vol 11 (5) ◽  
pp. 631-636 ◽  
Author(s):  
Wenderson Nascimento Lopes ◽  
Fabio Isaac Ferreira ◽  
Felipe Aparecido Alexandre ◽  
Danilo Marcus Santos Ribeiro ◽  
Pedro de Oliveira Conceição Junior ◽  
...  
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Digital Signal Processing ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Single Point ◽  
Digital Signal ◽  
Power Spectral

scholarly journals Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Procedia CIRP ◽  
2016 ◽  
Vol 41 ◽  
pp. 431-436 ◽  
Author(s):  
Doriana M. D’Addona ◽  
Davide Matarazzo ◽  
Paulo R. de Aguiar ◽  
Eduardo C. Bianchi ◽  
Cesar H.R. Martins
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Single Point ◽  
Tool Condition Monitoring ◽  
Tool Condition

scholarly journals Tool Condition Monitoring in Grinding Operation Using Piezoelectric Impedance and Wavelet Transform

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 10
Author(s):  
Pedro Oliveira Junior ◽  
Paulo Aguiar ◽  
Rodrigo Ruzzi ◽  
Salvatore Conte ◽  
Martin Viera ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Condition Monitoring ◽  
Single Point ◽  
Tool Condition Monitoring ◽  
Grinding Wheel ◽  
Discrete Wavelet ◽  
Electromechanical Impedance ◽  
Tool Condition ◽  
Dressing Tool ◽  
Piezoelectric Impedance

The purpose of the present study is to monitor tool condition in a grinding operation through the electromechanical impedance (EMI) using wavelet analysis. To achieve this, a dressing experiment was conducted on an industrial aluminum oxide grinding wheel by fixing a stationary single-point diamond tool. The proposed approach was verified experimentally at various dressing tool conditions. The signals obtained from an EMI data acquisition system, composed of a piezoelectric diaphragm transducer attached to the tool holder, were processed using discrete wavelet transform. The approximation and detail coefficients obtained from wavelet decomposition were used to estimate tool condition using the correlation coefficient deviation metric (CCDM). The results show excellent performance in tool condition monitoring by the proposed technique, which effectively contributes to modern machine tool automation.

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