Abstract
Tracking mill liner wear is essential for improved plant reliability and grinding performance. This study developed a novel IoT wear sensor and a Discrete Element Modelling coupled methodology to predict and continuously evolve shell liner’s wear pattern. The IoT sensor was purposely developed to track and report the live thickness of a shell liner. Global wear pattern was then obtained by coupling the qualitative wear intensity obtained in DEM and sensor results, from which a topological evolution model was established to generate the shell liner’s progressive wear profiles. Predictions of the wear evolution model were compared with 3D laser scan measurements collected during operation. Results indicated that the wear evolution model showed less than 8% error with laser scan measurements in quantitative wear rate comparison.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
