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Updated: Jan 10, 2026

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Reliable Mechanochemistry: Protocols for Reproducible Outcomes of Neat and Liquid Assisted Ball-mill Grinding Experiments
Published on: January 23, 2018
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Digital simulation of rock grinding process in ball mill.
Valentin A Chanturia1, Yuriy V Dmitrak2, Aleksey A Kravtsov1
1Institute of Comprehensive Exploitation of Mineral Resources RAS, Moscow, 111020, Russian Federation.
Scientific Reports
|November 29, 2025
Summary
This study developed a digital physical model for drum mill grinding loads, optimizing rock destruction and energy consumption using artificial intelligence. The model enhances grinding efficiency by considering material properties and operational parameters.
Area of Science:
- Materials Science and Engineering
- Mechanical Engineering
- Computational Mechanics
Background:
- Grinding load motion modeling is crucial for determining dynamic parameters and energy consumption in rock destruction processes within mills.
- Accurate modeling impacts the efficiency and energy usage of various mill types.
Purpose of the Study:
- To create a digital physical model of a drum mill's grinding load.
- To incorporate the physical properties of grinding rock into the model.
- To enable process optimization using artificial intelligence.
Main Methods:
- Finite element method (FEM) simulations using LS-DYNA (Ansys).
- Model creation and visualization with Altair Hypermesh and LS-Prepost.
- Post-processing of results using ParaView.
Main Results:
- Investigated rock piece destruction via impact and abrasive actions, considering material properties.
- Developed a generalized model of rock destruction incorporating material properties, particle sizes, and fine fraction influences.
- Identified rock grinding speed as an optimization criterion for mill design and operation.
Conclusions:
- The generalized model provides dynamic insights into grinding load behavior.
- Optimizing mill design and operational parameters can be achieved by focusing on rock grinding speed.
- The study enables energy consumption optimization for the grinding process.

