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Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach

1
Faculdade de Engenharia, UNESP-University Estadual Paulista, Bauru, Departamento de Engenharia Elétrica, Av. Eng. Luiz Edmundo C. Coube 14-01, 17033-360 Bauru–SP, Brazil
2
Dipartimento di Ingegneria Chimica, Università degli Studi di Napoli Federico II, dei Materiali e della Produzione Industriale; 80138 Napoli NA, Italy
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4453; https://doi.org/10.3390/s18124453
Received: 15 November 2018 / Revised: 27 November 2018 / Accepted: 14 December 2018 / Published: 16 December 2018
(This article belongs to the Special Issue Sensor Applications for Smart Manufacturing Technology and Systems)
This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation. View Full-Text
Keywords: sensor monitoring; tool condition monitoring; piezoelectric sensors; electromechanical impedance; dressing; grinding process; neural networks; MLNN; k-NN sensor monitoring; tool condition monitoring; piezoelectric sensors; electromechanical impedance; dressing; grinding process; neural networks; MLNN; k-NN
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Junior, P.; D’Addona, D.M.; Aguiar, P.; Teti, R. Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach. Sensors 2018, 18, 4453.

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