Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

Abstract

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as ''sharp'' (with cutting capacity) or ''dull'' (with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation

“…The use of neural network models for monitoring of grinding processes applies primarily to the modelling of interactions between the processing variables and the values explaining the state of abrasive tool or parameters of the processed surface parameters. The validity of using artificial intelligent methods for the estimation of processed surface roughness [17,18], tool wear [19][20][21] and precision of the shape and dimensions of processed elements [22] was demonstrated.…”

Estimação espectral do sinal de vibração para o monitoramento do desgaste do dressador de ponta única

“…Moia et al, [17] monitoraram a operação de dressagem com estatísticas derivadas do sinal de EA que serviram de entrada para o modelo de RNA. Os resultados foram bem sucedidos para classificar a condição do rebolo após a operação de dressagem, como sendo o mesmo afiado (com capacidade de corte) e em função do uso, como empastado (com perda da capacidade de corte).…”

Recognition of Acoustic Signals of Synchronous Motors with the Use of MoFS and Selected Classifiers

“…Modern diagnostic systems can diagnose early failure conditions of motors. These systems are based on the study of various signals such as: magnetic signals, ultrasounds, acoustic signals, images from the camera, vibroacoustic signals, and electric signals [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. In recent years, the methods of acoustic signal recognition were developed [25][26][27][28].…”