Tool condition monitoring of single-point dressing operation by digital signal processing of AE and AI

• Autor(a) principal: D'Addona, Doriana M.
• Data de Publicação: 2018
• Outros Autores: Conte, Salvatore, Lopes, Wenderson Nascimento [UNESP], Aguiar, Paulo R. de [UNESP], Bianchi, Eduardo C. [UNESP], Teti, Roberto, Teti, R., DAddona, D. M.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.procir.2017.12.218; http://hdl.handle.net/11449/197859
• topic: Dressing; Acustic emission signal; Vibration signal; Tool wear; Artificial neural networks

This work aims at determining the right moment to stop single-point dressing the grinding wheel in order to optimize the grinding process as a whole. Acoustic emission signals and signal processing tools are used as primary approach. An acoustic emission (AE) sensor was connected to a signal processing module. The AE sensor was attached to the dresser holder, which was specifically built to perform dressing tests. In this work there were three types of test where the edit parameters of each dressing test are: the passes number, the dressing speed, the width of action of the dresser, the dressing time and the sharpness. Artificial Neural Networks (ANNs) technique is employed to classify and predict the best moment for stopping the dressing operation. During the ANNs use, the results from Supervised Neural Networks and Unsupervised Neural Networks are compared. (C) 2017 The Authors. Published by Elsevier B.V.