Evaluating characteristics of particles’ surface micro-texture of granular materials based on the spectral analysis method

ABSTRACT

Accurately evaluating particle surface micro-texture levels and distribution at different wavelength scales of granular material is critical to optimise service stability of granular material under long-term dynamic loads. Firstly, generalised regression neural network (GRNN) and empirical mode decomposition (EMD) were adopted to impute missing data points and remove the arc-shaped tendency of granular materials’ particle wear raw surface profile. Then, granular materials’ particle surface micro-texture levels and their distribution within constant bandwidth narrow band spectrum and octave/fractional octave band spectrum were obtained with spectral analysis method, which could characterise granular materials’ particle surface properties at different wavelength scales. Fourteen types of parent rocks of granular materials were tested with a modified micro tribological experiment simulating tribological behaviour among particle contact interfaces under dynamic loads. A contrastive analysis with traditionally used surface mean roughness was performed. The results indicate that the micro-texture levels and their distribution obtained in this study can detect the level of the exact texture scales (i.e. 32 and 2 μm wavelengths) significantly influencing the kinetic friction coefficient of granular materials, while surface mean roughness can only represent the global surface property. A high correlation was found between normalised micro-texture level (i.e. 32 μm) and Moh's hardness and coefficient of friction.

KEYWORDS:

• Granular materials
• surface texture
• tribological behaviour
• spectral analysis
• generalised regression neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was sponsored by the Research Fund of the National Natural Science Foundation of China [grant number 51808462], the Inner Mongolia Autonomous Region Science and Technology Planning Project [2021GG0038], the Major Science and Technology Project of Hohhot [2021-Key-Social-3], the China Postdoctoral Science Foundation [grant number 2018M643520], the Applied Basic Research Project of Sichuan Science and Technology Department (Free Exploration Type) [grant number 2020YJ0039], and the Key R & D Support Plan of Chengdu Science and Technology Project – Technology Innovation R & D Project [grant number 2019-YF05-00002-SN]. These supports are gratefully acknowledged. The results and opinions presented are those of the authors and do not necessarily reflect those of the sponsoring agencies.