Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

• Autor(a) principal: Martins, Cesar H. [UNESP]
• Data de Publicação: 2013
• Outros Autores: Aguiar, Paulo R. [UNESP], Bianchi, Eduardo C. [UNESP], Frech, Arminio [UNESP], Ruzzi, Rodrigo S. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.2316/P.2013.793-015; http://hdl.handle.net/11449/75065
• topic: Acoustic emission; Dresser wear; Dressing operation; Kohonen neural network; Multilayer perceptron; Neural network; Acoustic emission signal; Finishing process; Grinding operations; Harmonic contents; Its efficiencies; Kohonen network; Kohonen neural networks; Multi layer perceptron; Acoustic emissions; Grinding wheels; Intelligent systems; Neural networks; Grinding (machining)

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.