Dynamic characteristic parameters identification for machine tool sliding joints based on neural network modeling

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (7) : 109-115.

ZHUJianmin, ZHOU Yanan, HEDandan, ZHENGZhouyang

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Abstract

Aiming at the problem that dynamic characteristic parameters of sliding joints of a machine tool are difficult to identify accurately, here, a neural network modeling method for identifying sliding joints’ dynamic characteristic parameters was proposed. The model’s variables to be optimized were stiffness parameters and damping ones of sliding joints. With the calculation results of the neural network model and those of the machine tool test modal analysis, stiffness and damping parameter identification of the sliding joints was performed with the cuckoo optimization algorithm. As an example, modeling, tests and parametric identification were conducted for a self-designed and manufactured sliding guide joints between the working platform and the machine tool body. The results showed that the proposed method is feasible and effective; the parametric identification accuracy is higher than those recorded in literatures.

Key words

sliding joint / neural network modeling / dynamic characteristic parameters / optimal identification / test

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ZHUJianmin, ZHOU Yanan, HEDandan, ZHENGZhouyang. Dynamic characteristic parameters identification for machine tool sliding joints based on neural network modeling[J]. Journal of Vibration and Shock, 2018, 37(7): 109-115

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