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

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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Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1—PZT Diaphragm Transducer Response and EMI Sensing Technique

Sensors ◽

10.3390/s18124455 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4455 ◽

Cited By ~ 9

Author(s):

Pedro Junior ◽

Doriana M. D’Addona ◽

Paulo R. Aguiar

Keyword(s):

Condition Monitoring ◽

Lead Zirconate Titanate ◽

Electromechanical Coupling ◽

Tool Condition Monitoring ◽

Band Frequency ◽

Electromechanical Impedance ◽

Grinding Processes ◽

Damage Indices ◽

Tool Condition ◽

Dressing Tool

Low-cost piezoelectric lead zirconate titanate (PZT) diaphragm transducers have attracted increasing attention as effective sensing devices, based on the electromechanical impedance (EMI) principle, for applications in many engineering sectors. Due to the considerable potential of PZT diaphragm transducers in terms of excellent electromechanical coupling properties, low implementation cost and wide-band frequency response, this technique provides a new alternative approach for tool condition monitoring in grinding processes competing with the conventional and expensive indirect sensor monitoring methods. This paper aims at assessing the structural changes caused by wear in single-point dressers during their lifetime, in order to ensure the reliable monitoring of the tool condition during dressing operations. Experimental dressing tests were conducted on aluminum oxide grinding wheels, which are highly relevant for industrial grinding processes. From the results obtained, it was verified that the dresser tip diamond material and the position of the PZT diaphragm transducer mounted on the dressing tool holder have a significant effect on the sensitivity of damage detection. This paper contributes to the realization of an effective monitoring system of dressing operations capable to avoid catastrophic tool failures as the proposed sensing approach can identify different stages of the dressing tool lifetime based on representative damage indices.

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Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microturning of Titanium Alloy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.984-985.31 ◽

2014 ◽

Vol 984-985 ◽

pp. 31-36 ◽

Cited By ~ 1

Author(s):

A. Gopikrishnan ◽

A.K. Nizamudheen ◽

M. Kanthababu

Keyword(s):

Acoustic Emission ◽

Titanium Alloy ◽

Tool Wear ◽

Condition Monitoring ◽

Dominant Frequency ◽

Tool Condition Monitoring ◽

Discrete Wavelet ◽

Nose Radius ◽

Emission Signal ◽

Tool Condition

In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AERMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.

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

Procedia CIRP ◽

10.1016/j.procir.2017.12.218 ◽

2018 ◽

Vol 67 ◽

pp. 307-312 ◽

Cited By ~ 5

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

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Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Procedia CIRP ◽

10.1016/j.procir.2016.01.001 ◽

2016 ◽

Vol 41 ◽

pp. 431-436 ◽

Cited By ~ 24

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

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Tool condition monitoring of aluminum oxide grinding wheel using AE and fuzzy model

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-018-1582-0 ◽

2018 ◽

Vol 96 (1-4) ◽

pp. 67-79 ◽

Cited By ~ 32

Author(s):

Felipe Aparecido Alexandre ◽

Wenderson Nascimento Lopes ◽

Fábio R. Lofrano Dotto ◽

Fábio Isaac Ferreira ◽

Paulo Roberto Aguiar ◽

...

Keyword(s):

Aluminum Oxide ◽

Condition Monitoring ◽

Fuzzy Model ◽

Tool Condition Monitoring ◽

Grinding Wheel ◽

Tool Condition

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Progressive tool condition monitoring of end milling from machined surface images

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405416640417 ◽

2016 ◽

Vol 232 (2) ◽

pp. 251-266 ◽

Cited By ~ 5

Author(s):

Samik Dutta ◽

Surjya K Pal ◽

Ranjan Sen

Keyword(s):

Condition Monitoring ◽

Flank Wear ◽

End Milling ◽

Tool Condition Monitoring ◽

Discrete Wavelet ◽

Tool Flank Wear ◽

Tool Condition ◽

Milled Surface ◽

Occurrence Matrix ◽

Surface Images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values.

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