Development of Intelligent Identification of Cutting States by Spectrum Analysis for CNC Turning

Abstract

In order to realize the intelligent machine tools, which can autonomously determine the cutting states and can change them automatically as required due to changes in the environmental conditions, an in-process monitoring and identification of cutting states is developed for CNC turning machine to check and improve the stability of the processes. The method developed utilizes the power spectrum density, or PSD of dynamic cutting force measured during cutting. Experimental 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 in order to realize safe and reliable machining. The proposed method introduces three ratios, which are calculated from three dynamic cutting force components and obtained by taking the ratio of cumulative power spectrum density for a certain frequency range corresponding to the states of cutting to that of the whole frequency range of each dynamic cutting force component, to classify the cutting states of continuous chip formation, broken chip formation, and chatter. 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. 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.

Monitoring of chip breaking and surface roughness in computer numerical control turning by utilizing wavelet transform of dynamic cutting forces

“…The dynamic cutting forces are relatively large in amplitude when the chips are broken into pieces and the relatively large PSDs appear in the higher frequency range, which corresponds to the chip breaking frequency, typically more than 50 Hz due to the shorter chip length. 18,19 The experimentally obtained dynamic cutting forces are correspondent with the experimentally obtained surface roughness profile as shown in Figures 8 and 9. The FFT is utilized to check the frequency of the dynamic cutting forces and the surface roughness profile, which are the same frequency of 24 Hz as shown in Figure 9.…”

“…But their PSDs of dynamic cutting forces are relatively small, which compare to the one from the surface roughness profile, and appear at low-frequency range due to the longer chip length. 18,19…”

“…But their PSDs of dynamic cutting forces are relatively small, which compare to the one from the surface roughness profile, and appear at lowfrequency range due to the longer chip length. 18,19 The dynamic cutting forces can be decomposed by the Meyer wavelet transform into many levels for the detail signals and the approximate signals to analyze the surface roughness signal and the broken chip signal. The different levels have been sensitive to the different frequency intervals.…”

“…Since the amplitudes of dynamic cutting forces are relatively large when the broken chips appear during the cutting which leads to the difficulty to estimate the surface roughness. 18,19 Moreover, the surface roughness profile cannot be identified from the dynamic cutting forces while the broken chips happen because the surface roughness signal is dominant by the effect of chip breaking frequency and its amplitude in both time and frequency domains. 2,3,20,21 Hence, it is necessary to decompose the dynamic cutting forces in order to classify the broken chip signals from the surface roughness signal in order to develop the in-process prediction of surface roughness models.…”

A method to evaluate stiffness and damping parameters of cabin suspension system for heavy truck

“…In some cases, system identification method is a good way to obtain parameters unknown. 12,13 Using integral method, damping identification in frequency domain was studied by Guo et al 14 An identification method for damping ratio in rotor systems was presented by Wang et al 15 Do et al 16 presented a new approach of friction model for tendon-sheath actuated surgical systems and made parameter identification. The identification of the manipulator stiffness model parameters in industrial environment was studied by Klimchik et al 17 Thus, in order to obtain all the accurate stiffness and damping parameter values of the cabin system conveniently and fast, the parameters identification method is a better choice.…”