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.
Neste trabalho, buscou-se observar a atuação de três tipos de diamantes, dois naturais e um sintético, para determinar qual deles apresenta vida útil maior ou com maior desgaste em relação aos demais. A metodologia empregada nesse estudo foi o de dressar um rebolo de óxido de alumínio e, a cada vinte passadas com um avanço da profundidade de dressagem de 40 µm por passada, tirar uma imagem da ponta na posição horizontal e outra na posição vertical deste diamante num microscópio estereoscópio. Após isso, o dressador foi recolocado na retificadora para a realização de uma nova dressagem. O critério para o fim do ensaio foi o faiscamento, gerado pelo contato do rebolo com a base do dressador, que indicou o fim do contato do diamante com o rebolo. Após os ensaios, foram calculadas as áreas gastas de cada diamante de acordo com cada imagem obtida por meio do auxilio de um software gráfico. Os dados foram então dispostos em gráficos.
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