Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

中文翻译：

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基于深度置信网络的鞍面转子同轴度和垂直度预测

目的

针对马鞍面型多级转子装配误差传递机制尚不明确的问题，本文旨在提出深度置信网络，实现多级转子同轴度和垂直度的预测。级转子。

设计/方法/途径

首先对航空发动机转子的表面型式进行分类。将转子表面轮廓采样数据转化为图像结构数据，建立了基于卷积神经网络的转子表面类型分类器。然后，针对鞍面转子，建立了基于深度置信网络的同轴度和垂直度预测模型。为验证本文提出的同轴度和垂直度预测方法的有效性，进行了多级转子同轴度和垂直度装配测量实验。

发现

论文结果表明，使用卷积神经网络进行人脸类型分类的准确率为99%，满足后续拼装过程的要求。对于80组测试样本，深度信念网络预测方法的同轴度和垂直度的平均误差分别为0.1和1.6 µm。

原创性/价值

因此，本文提出的方法不仅可以用于转子表面分类，还可以用于指导航空发动机多级转子的装配。