Title

Intelligent Cable Shovel Excavation Modeling and Simulation

Abstract

Cable shovel excavators are used for primary production of geomaterials in many surface mining operations. A major problem in excavation is the variability of material diggability, resulting in varying mechanical energy input and stress loading of shovel dipper-and-tooth assembly across the working bench. This variability impacts the shovel dipper and tooth assembly in hard formations. In addition, the geometrical constraints within the working environment impose production limitations resulting in low production efficiency and high operating costs. An intelligent shovel excavation (ISE) technology has been proposed as a potential solution to these problems. This paper addresses the requirements of the dynamic models of the cable shovel underlying the ISE technology. The dynamic equations are developed using the Newton-Euler techniques. These models are validated with real-world data and simulated in a virtual prototype environment. The results provide the path trajectories, dynamic velocity and acceleration profiles, and dimensioned parameters for optimum feed force, torques and momentum of shovel boom-dipper assembly for efficient excavation. The optimum digging forces and resistances for the cable shovel excavators are modeled and used to predict optimum excavation performance.

Department(s)

Mining and Nuclear Engineering

Keywords and Phrases

Artificial Intelligence; Excavation; Mining; Simulation

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2008 American Society of Civil Engineers (ASCE), All rights reserved.


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