Issue |
Mechanics & Industry
Volume 25, 2024
|
|
---|---|---|
Article Number | 36 | |
Number of page(s) | 13 | |
DOI | https://doi.org/10.1051/meca/2024031 | |
Published online | 20 December 2024 |
Regular Article
Comparative analysis study of resistance characteristics of backhoe hydraulic excavators
1
School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China
2
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China
* e-mail: ZguiRen2014@163.com
Received:
28
March
2023
Accepted:
23
October
2024
Resistance characteristics research lays a foundation for establishing and improving excavator performance evaluation. Therefore, a thorough understanding of the general laws governing excavation resistance is particularly significant. Based on experimental data from 8 sets of excavation conditions involving two types of a 20 t backhoe hydraulic excavator, this paper first conducted a comparative analysis of the distribution trends and concentration of resistance coefficients, resistance moment coefficients, resistance angles, differential angles, and component rotation angular velocities. Subsequently, employing the response surface optimization theory, the main value intervals of relevant data under different conditions were obtained, and the impact of excavation scenarios and type variations on the distribution of these intervals was explored. Finally, the principal value intervals under different conditions were applied to calculate and verify the theoretical digging force. The results indicate differences in the general laws of resistance characteristics under different conditions, with the machine type having a more significant influence on the main value intervals than the excavation condition. Variations in the main value intervals lead to changes in the performance evaluation metrics of the excavator. Under different conditions, the front-end working unit of the excavator maintains a stable operational speed.
Key words: Excavators / numerical simulation / optimization / statistics
© T. Li et al., Published by EDP Sciences 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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