Quantitative identification of slider nanoscale wear based on the head-disk interface dynamics. (December 2017)

• Record Type: Journal Article
• Authors: Yang, Ang; Wang, Yu; Zi, Yanyang; Liang, Xianghua
• Is Part Of: Tribology international. Volume 116(2017)
• Page Start: 95
• Page End: 104
• Publication Date: 2017-12
• Subjects: Head disk interface -- Slider dynamics -- Wear mechanism -- Quantitative identification; Tribology -- Periodicals; 621.89
• DOI: 10.1016/j.triboint.2017.06.044
• Languages: English
• ISSNs: 0301-679X

Abstract

Abstract: To reveal the mechanism of head-disk interface wear behaviors, this paper proposed a novel transformation on slider's vibration signals, which was derived to having a strong correlation with the slider wear. Based on the modal analysis, the high-frequency component was regarded as the air bearing mode while the low-frequency corresponds to the structure (suspension related) mode. Both structure and air bearing mode dissipated energies were extracted and the correlation analysis between the energy and wear volume verified the proportional relationship. Finally, regression analysis was employed to establish the linear model for wear and energy. This paper helps understand the wear mechanisms of slider from the interface dynamics response's view, and then provides a novel quantitative analysis approach for slider wear identification. Highlights: A novel transformation on slider's vibration which was derived to having a strong correlation with the slider wear. Identification of spectrum components of slider based on finite element method and modal analysis. The energy extraction strategy and correlation analysis were applied for correlating the energy and wear volume. The quantitative slider wear identification approach with regression analysis.