Dynamic Compensation for Contact Probe Based on Genetic Algorithm and Elman Neural Network

doi:10.3901/JME.2022.10.024

Abstract

Abstract: The undesired dynamic characteristic is an important source for dynamic measurement errors of contact probe systems, which greatly restricts the improvement of measurement speed and precision. A dynamic compensation method based on genetic algorithm(GA) and Elman neural network(ENN) is presented to compensate the dynamic characteristics of probes. The genetic algorithm is used to optimize the ENN method to dynamically compensate the output signal of dynamic response. The adaptive recursive least-square method is used to identify the dynamic models of the probe system before and after compensation. The dynamic measurement uncertainty of the probe system is reduced from 77.8 nm to 12.1 nm. The global search ability of GA is utilized to overcome ENN’s shortcoming of easy convergence to the local extreme values. This method has fast network training speed and high dynamic compensation precision. The effectiveness of this method is verified by the simulation analysis and the uncertainty evaluation results.

Key words: contact probe, dynamic compensation, genetic algorithm, elman neural network, measurement uncertainty

CLC Number:

TP212

CHENG Zhenying, JIANG Wenshu, FANG Xu, LI Ruijun, HUANG Qiangxian. Dynamic Compensation for Contact Probe Based on Genetic Algorithm and Elman Neural Network[J]. Journal of Mechanical Engineering, 2022, 58(10): 24-30.

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