Development and testing of a turning process monitoring system using acoustic emission / Keiichi Ninomiya … [et al.]

Studies on in-process measurement have suggested techniques for sensing tool wear and machine status in a machining center and turning center, for example by measuring cutting resistance. However, there appear to be no reports of effective uses of machine sensing in practical machine tools, perhaps due to problems such as limitations on the number and density of sensors and the possibility that attaching sensors may affect machine rigidity and thus processing quality. The purpose of this research is to develop a system that can monitor the state of the cutting process and maintain it in a normal or optimal state at all times, based on the acoustic emission (AE) method. To realize the development of such a system, it is very important to evaluate the tool wear qualitatively and quantitatively. This report describes a tool wear experiment that collected AE signals, cutting force data, and high-speed camera images during metal turning. The collected basic data are comprehensively evaluated and examined to determine the effectiveness of inprocess measurement by the AE method. The results suggest that evaluating various AE parameters obtained from the AE original waveform is an effective parameter for monitoring tool wear and cutting conditions that affect product quality. The findings from the basic data obtained in this study were found to be useful information for the practical application of in-process measurement by the AE method using machine learning methods.