A Reliable Analysis Method for Estimating Large Excavator Structural Strength
Abstract
A dynamic analysis method is proposed for evaluating the structural strength of large excavators. The method is validated by employing a heavy mining shovel. The front-end is created as a flexible multi-body dynamics system using Lagrange equations and finite element technology. The model loadings are constructed from the machine motion, operation, and inertia. The variable amplitude stress ranges are recovered from structural dynamics simulation, and then are transformed to equivalent constant amplitude stress range with the rainflow counting method and Palmgren-Miner's cumulative damage rule. The result comparisons from the simulation with the measured field data and static analysis show that the dynamic analysis gives a higher safety confidence in the shovel front-end stress simulation than the static analysis. The validated model is used to predict the longevity of shovel structural components. The derived analysis method can be applied to any mining excavators for the fatigue life prediction.
Recommended Citation
Y. Li et al., "A Reliable Analysis Method for Estimating Large Excavator Structural Strength," Handbook of Materials Failure Analysis with Case Studies from the Chemicals, Concrete and Power Industries, pp. 243 - 258, Elsevier, Jan 2016.
The definitive version is available at https://doi.org/10.1016/B978-0-08-100116-5.00010-7
Department(s)
Mining Engineering
Keywords and Phrases
Construction equipment; Dynamic analysis; Equations of motion; Excavation; Excavators; Fatigue of materials; Shovels; Static analysis; Strength of materials; Structural Dynamics; Dynamic analysis method; Fatigue life prediction; Finite element technology; Flexible multi-body dynamics; Rain flow counting method; Stress range; Structural dynamics simulation; Validation; Structural analysis; Fatigue life; Validation
International Standard Book Number (ISBN)
978-0-08-100116-5
Document Type
Book - Chapter
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Elsevier, All rights reserved.
Publication Date
01 Jan 2016