Work

Investigating the Wear Evolution and Shape Optimize of SAG Mill Liners by DEM-FEM Coupled Simulation

Year: 2025

Type: article

Abstract: The shell liner is a core component of Semi-Autogenous Grinding (SAG) mills, suffering severe wear from ore impact and friction, and its shape directly affects grinding efficiency and maintenance costs. In this study, the Finnie wear model in EDEM2022 software was improved to predict the wear morphology evolution of shell liners. A Python-based coupled simulation of the Discrete Element Method (DEM, EDEM) and Finite Element Method (FEM, ABAQUS) was established to analyze liner wear mechanisms, stress states, and mill service performance (wear resistance, grinding efficiency, and stress distribution). The simulated wear profile showed high consistency with laser three-dimensional scanning (LTDS) results, confirming the improved Finnie-DEM model’s effectiveness in reproducing liner wear evolution. Shearing in crushing/grinding zones was the main wear cause, with additional contributions from relative sliding among ore, grinding balls, and liners in grinding/discharge zones. DEM-FEM coupling revealed two circumferential instantaneous wear extremes (Maxa > Maxb) and two lifter wear rate peaks (Ma > Mb). In the grinding zone, liner stress distribution matched wear distribution, with maximum instantaneous stress at characteristic points A and B—stress at A reflects liner impact degree, while stress at B indicates mill ore-crushing capacity. Optimizing flat liner shape adjusted wear rate peaks (Ma, Mb), improving overall liner wear. This optimization significantly affected stresses at A/B and ore normal collision but had little impact on mill energy efficiency.

Source: Minerals

DOI: 10.3390/min15111155

Authors: Xiao Mei, Huicong Du, Wenju Yao, Aibing Liu

Institution: Shanghai Maritime University

Language: English

Topic: Mineral Processing and Grinding

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Shanghai Maritime University