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Study on robotic projectile launching based on multi-factor analysis and parameter optimization
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DOI：10.3389/fmech.2025.1707301.png)
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Abstract
En 中文
The precision of projectile launching mechanisms, which utilize counter-rotating friction wheels, is critical for system effec-tiveness. This study introduces a hybrid approach combining multi-physics simulation with an intelligent optimization algo-rithm to determine key design parameters. Initially, Finite Element Analysis (FEA) and kinematics simulations were conducted on a 3D model to generate a comprehensive dataset linking operational conditions to projectile dynamics. This dataset then served to train a neural network for velocity prediction. Subsequently, a genetic algorithm was implemented to optimize the friction coefficient and inter-wheel gap by targeting a desired exit velocity range. The proposed methodology successfully identifies optimal parameter configurations, offering a robust, data-driven solution to a complex design challenge.
Keywords:
projectile launch
kinematic simulation
finite element analysis
neuralnetwork
parameter optimization
Journal
F
IF:
3
Papers:
143
Citations:
0