This paper investigates the machining stability in ball-end-milling of curved surface in which the inclination of tool continuously changes. Initially, the influence of inclination angle is geometrically investigated on the parameters such as immersion angle and cutting velocity. Then, the paper presents the stability lobe diagrams of the process. Curved surface milling is simulated by slot milling on a cylindrical workpiece using a ball-end-mill to obtain the cutting force and vibration, which are used for fast-Fourier transform and Hilbert-Huang transform (HHT) analyses. Experimental results show that the cutting force increases, and the stability becomes worse with the inclination angle, while the machining errors decrease with the inclination. The vibration analysis showed that the HHT can detect the transition from stable to unstable during milling of curved surface in the time-frequency plots.
In face milling process of train wheel, cutter is one of the most important part and this part should be monitored from failure phenomena for improving final products of train wheel during machining process. One of the best ways for face milling tool condition monitoring is by analyzing signal. However, noise usually contaminates the measured signals during measurement using any sensors. This study presents the effect of noise on the Hilbert-Huang transform features for face milling condition monitoring by mean analyzing the synthetic vibration signals. First, noisy synthetic signals were created, then separates them by Empirical Mode Decomposition (EMD) to be intrinsic mode decompositions (IMFs). Second, the Hilbert-Huang spectra were generated and then compared to HHT baseline spectrum. The result showed that the noise disturbed the HHT spectrum. Without filtering signal, the face milling cutter condition phenomenon was difficult to be revealed by HHT.
Turn-milling is a kind of machining process which widely applied in industries with large-sized workpieces. It is because turn-milling provides advantages for machining large-diameter mechanical parts with high speed, reducing cutting temperature due to the chips being broken, which in turn decreases tool wear. However, it needs to monitor the turn-milling process for preventing the onset of chatter during operation. It is due to the chatter becoming a limitation to productivity, part quality, accelerates tool wear, and machine-tool damage. One of the ways for turn-milling process monitoring is by vibration analysis. Acquired vibrations in the machining process are generally backgrounded by noises and the conventional filtered tools may have defiance for reducing them. It is significant to find signal processing tools for denoising noisy signals before further analysis. This paper presents the utilization of the empirical mode decomposition (EMD) method as an efficient and adaptive noise filter. The Short-Time Fourier Transform (STFT) improvement using EMD is then used for monitoring turn-milling process conditions in the energytime-frequency domain. The results showed that the reconstructed signal was quite impressive compared to the raw signal and the oscillation of the filtered signal was clearer than the raw signal. The improvement of the filtered signals was proved by the kurtosis index and spectral kurtosis. The kurtosis index had been improved 10 – 27 times more than raw signals. The improved STFT using EMD showed a significant spectrum with high resolution compared to conventional STFT. The energy density could be observed clearly in the machining characteristic frequencies with an improvement of about 10-100 times larger. The proposed method is therefore effectively applied to monitor the turn-milling condition.
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