Aiming at the problem that the transmission mechanism of the assembly error of the multi-stage rotor with saddle surface type is not clear, the purpose of this paper is to propose a deep belief network to realize the prediction of the coaxiality and perpendicularity of the multi-stage rotor.
First, the surface type of the aero-engine rotor is classified. The rotor surface profile sampling data is converted into image structure data, and a rotor surface type classifier based on convolutional neural network is established. Then, for the saddle surface rotor, a prediction model of coaxiality and perpendicularity based on deep belief network is established. To verify the effectiveness of the coaxiality and perpendicularity prediction method proposed in this paper, a multi-stage rotor coaxiality and perpendicularity assembly measurement experiment is carried out.
The results of this paper show that the accuracy rate of face type classification using convolutional neural network is 99%, which meets the requirements of subsequent assembly process. For the 80 sets of test samples, the average errors of the coaxiality and perpendicularity of the deep belief network prediction method are 0.1 and 1.6 µm, respectively.
Therefore, the method proposed in this paper can be used not only for rotor surface classification but also to guide the assembly of aero-engine multi-stage rotors.
This work was supported by Heilongjiang Provincial Natural Science Foundation of China (grant number JJ2020LH0172), National Key R&D Program of China (grant number 2021YFF0603200), China Postdoctoral Science Foundation (grant number 2021T140164), Heilongjiang Postdoctoral Fund (grant number LBH-TZ2112) and National Natural Science Foundation of China (grant number 52175498).
Sun, C., Wang, Y.C., Lu, Q., Liu, Y. and Tan, J. (2022), "Coaxiality and perpendicularity prediction of saddle surface rotor based on deep belief networks", Assembly Automation, Vol. 42 No. 6, pp. 761-772. https://doi.org/10.1108/AA-06-2022-0163
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