Morphological Construction of Transmission Error of Precision Reducer Based on BP Neural Network

Abstract

Transmission error is a key indicator to characterize the performance of precision reducer. The test result of transmission error will be influenced by input speed and load torque. In this study, a morphological construction method of transmission error of precision reducer based on BP neural network was proposed to obtain the morphology of transmission error under different input speeds and load torques. Then the transmission accuracy of precision reducer could be accurately evaluated. With RV-40E reducer as an example, the morphological structure of its transmission error was obtained. The experimental results demonstrate that the model established by the proposed method can reflect the morphology of transmission error.