Error-Compensation Method for Inclination Measurement Under the Influence of the Dynamic Interference

Zhang, Yanshun; Guo, Yajing; Li, Kui; Pei, Chaoying; Li, Ming

doi:10.1109/jsen.2015.2491369

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Gang et al [16] proposed a new type of hypoid gear with actual tooth surface error sensitivity, which improves the basic design performance. Zhang et al [17] proposed a new method to effectively compensate for the inclination measurement error caused by rotating and accelerating linear motion. Liu et al [18] provided an accurate mathematical model considering the error of work holding equipment and established the correspondence between transmission error and gear geometry.…”

Section: Introductionmentioning

confidence: 99%

Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm

Chu,

Zhou

2024

Processes

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Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm.

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Section: Introductionmentioning

confidence: 99%