Effect of Key Parameters on Ploughing Force Performance of Planing-Type Anti-Climbers
This paper proposes a mathematical model-based analytical approach to address the cutting force prediction and performance optimization challenges in planing-type anti-climbers for high-speed train passive safety systems. The method overcomes the reliance on experimental calibration inherent to conv...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/5/353 |
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| Summary: | This paper proposes a mathematical model-based analytical approach to address the cutting force prediction and performance optimization challenges in planing-type anti-climbers for high-speed train passive safety systems. The method overcomes the reliance on experimental calibration inherent to conventional approaches, enabling the efficient quantitative evaluation of anti-climber cutting performance. By equivalently modeling the collision energy dissipation process as an orthogonal cutting model, a theoretical framework integrating material dynamic response characteristics and impact boundary conditions was developed for direct cutting force prediction without experimental calibration. Finite element modeling implemented on the ABAQUS platform was employed for simulation analysis, supplemented by dynamic impact tests for validation. The results demonstrate that the model achieves ≤15% relative error compared with the simulation data and ≤5% deviation from the experimental measurements, confirming its engineering applicability. Sensitivity analysis reveals that cutting depth exhibits the most pronounced positive correlation with cutting force, while increased tool rake angle reduces cutting force. The dynamic equilibrium between thermal softening effects and strain rate strengthening leads to cutting force reduction with elevated cutting speed. This research establishes theoretical and technical foundations for the intelligent optimization of passive safety systems in rail transit equipment. |
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| ISSN: | 2075-1702 |