Transmission Error Prediction of RV Reducer based on SSA-BP - Houzhen Sun; Hongbing Yuan; Bin Yu (Jixie chuandong)
There are many factors affecting the assembly quality of RV （Rotate Vector） reducer， and the assembly quality is unstable. In response to this situation， an optimized BP neural network quality prediction model based on the Sparrow Search Algorithm （SSA） is established. The transmission error of the key performance index of the reducer is used as the output index， and the key size parameters of the assembly parts are selected as the input influencing factors. After data preprocessing， use the quality prediction model to make predictions. The results show that the BP neural network prediction model improved by the Sparrow algorithm has good prediction accuracy， can effectively predict the selected index parameters， and provides help for the selection of parts in the assembly link of the RV reducer.