Abstract
Acoustic emission (AE) or vibration signal has been applied in detecting operations of grinding mills in many industries. This paper proposes an approach to generate AE signals based on the particle-wall impacts. Through a combination of multi-mode vibrations and the calibration of the key parameters, the model was able to reproduce experimental data. The AE model was then implemented into a discrete element method (DEM) modelling of particle flow in a rotating mill. The AE signals of the mill under different filling levels and rotation speeds were generated and analysed, mainly focusing on the frequency and magnitude of each vibration mode. The link between the AE signals and the particle-wall impact energy was explored.
Highlights
Milling is an important size reduction process in many industries, such as mineral and coal industries [1, 2]
Research has been conducted to establish the link between the Acoustic emission (AE) signal and internal particle flow dynamics
We developed an AE signal model based on particle-wall collisions
Summary
Milling is an important size reduction process in many industries, such as mineral and coal industries [1, 2]. Tang et al [2, 7, 8] analysed vibration signals in a wet ball mill and divided the signals into three parts, corresponding to (from low to high) natural frequency, main impact frequency and secondary impact frequency bands They developed a modelling strategy based on both vibration and acoustic signals [9]. To generate information more readily measurable in experiments, Hosseini et al [4] simulated the AE signals in a tumbling mill based on the impact force and system response. They established a system response function to an impact which was implemented into DEM simulations. The characteristics of the signals were analysed and linked to particle-wall impact energy
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