Intelligent identification of cutting states by utilising Power Spectrum Density

Purpose: This paper presents an in-process monitoring and identification of cutting states in turning process in order to realize the intelligent machine tools. Design/methodology/approach: The developed method utilizes the power spectrum density, or PSD of dynamic cutting force measured during the cutting. The experimentally obtained results suggested that there are basically three types of patterns of PSD when the cutting states are the continuous chip formation, the broken chip formation, and the chatter. The broken chip formation is desired to realize safe and reliable machining. The method proposed introduces three ratios, which are calculated and obtained by taking the ratios of cumulative PSD for a certain frequency range of three dynamic cutting force components corresponding to those three states of cutting, to classify the cutting states. The algorithm was developed to calculate the values of three ratios during the process in order to obtain the proper threshold values for classification of the cutting states. Findings: The method developed has been proved by series of cutting experiments that the states of cutting are well identified regardless of the cutting conditions. The broken chips are easily obtained by changing the cutting conditions during the processes referring to the algorithm developed. Practical implications: There are still not many systems being used in practice mainly due to the lack of general applicability such as a requirement of automated machining systems regardless of the cutting conditions. The aim of this research is to develop an in-process monitoring system for identification of continuous chip, broken chip, and chatter regardless of the cutting conditions by spectrum analysis based on the power spectrum density, or PSD of dynamic cutting force measured during the cutting. Originality/value: The largest potential advantage of the method proposed in this paper is that the states of cutting can be readily identified during the in-process cutting under any cutting conditions by simply mapping the experimentally obtained values of parameters referring to the proper threshold values in the reference feature spaces.