Surface roughness monitoring application based on artificial neural networks for ball-end milling operations

Surface roughness plays an important role in the performance of a finished part. The roughness is usually measured off-line when the part is already machined, although in recent years the trend seems to have been to focus on online monitoring. Measuring and controlling the machining process is now possible thanks to improvements and advances in the fields of computers and sensors. The aim of this work was to develop a reliable surface roughness monitoring application based on an artificial neural network approach for vertical high speed milling operations. Experimentation was carried out to obtain data that was used to train the artificial neural network. Geometrical cutting factors, dynamic factors, part geometries, lubricants, materials and machine tools were all considered. Vibration was captured on line with two piezoelectric accelerometers placed following the X and Y axes of the machine tool.