Abstract
Friction between metals is a physical phenomenon that occurs in manufacturing machine tools. This annoying noise implies unnecessary metal contact and deterioration of a mechanical system. In this study, for the monitoring of the friction between two metal surfaces, the acoustic signature was extracted by applying the wavelet transform method to the noise measured from the change in contact force for each state of adhesive and abrasive wear. Experiments were conducted with a constant relative speed between the contacting metal surfaces. For the adhesive wear, the peak signal-to-noise ratio (PSNR) calculated by the wavelet transformation increases with the increasing contact pressure. Opposite trends were observed for the abrasive wear. The proposed index formed a group within a specific range. This ratio exhibited a strong relationship with the wear characteristics and the surface condition. From the proposed index calculated by the wavelet coefficients, the continuous monitoring of the wear influence on the failure of the machine movement operations is achieved by the sound radiation from the contacting surfaces.
Highlights
For machine tools, moving components for material processing often come into contact with each other
The abrasive wear did not have a significant difference in the detail coefficient values compared to those of the adhesive wear
peak signal-to-noise ratio (PSNR) increased as the contact pressure increased, as was observed in the measured sound pressure in Figure 7a, but there was no tendency for level 1 as expected
Summary
For machine tools, moving components for material processing often come into contact with each other. Shih and Rigney [1] studied the variation of friction coefficients depending on the material coated on one or both sliding metal surfaces. Hase et al [11] studied the characteristics of sounds from abrasive and adhesive wears in frequency domain. For production with minimal output of defective parts, interference of tools or parts should be monitored in real-time For this reason, research on signal processing methods for the self-diagnosis of machines and the use of artificial intelligence (AI) has been expanding recently. The feature extraction method from the acoustic response is used to investigate the adhesive and abrasive wears between two metal materials. The extraction of features of friction sounds with variation of the contact pressure was reflected by the proposition of a system capable of self-diagnosis
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