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ABSTRACT The paper presents the latest results of sensing the cutting process on the basis of AE signals and some particularities in further development of the monitoring model for the finish turning process. Due to non-linearity, the large number of influencing parameters and missing information in AE data, the Artificial Neural Networks were chosen as a monitoring decision tool. The problem of accurateness in predicting the surface roughness on the basis of AE - because of the mutual interdependence of the data - requires a special procedure for building a neural network model. The final aim of such an approach is presented as improvements in learning or considerable reduction in error prediction. Further development of the monitoring model has the goal of building a so-called intelligent sensor, which should be able to perform the signal conditioning and feature extraction process. INTRODUCTION Most of the reports on research into the machining processes usually start with a similar ascertainment: the complexity of the cutting process is one of the main obstacles to successful modeling or monitoring of processes; this fact gives us the impetus to continue permanent investigations. There are no simple answers or quick solutions; a reliable monitoring approach or a complete control system for the cutting process is a task in which successful solutions could be obtained only through numerous, systematic investigations covering the different scientific areas incorporating sensor technologies, signal processing techniques, modeling methods, etc.
Approximate Word count = 804
Approximate Pages = 3.2
(250 words per page double spaced)
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