【摘要】 RV(Rotate Vector)减速器装配质量的影响因素众多，装配质量不稳定。针对此情况，建立了基于麻雀搜索算法（Sparrow search algorithm,SSA）优化的BP神经网络质量预测模型，以减速器的关键性能指标——传动误差作为输出指标，选取减速器零部件的关键尺寸参数作为影响因素输入，经过数据预处理后建立质量预测模型进行误差预测。结果表明，经过麻雀搜索算法改进后的BP神经网络预测模型具有良好的预测精度，为RV减速器装配环节的零件选配工作提供了帮助。更多还原

【Abstract】 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.更多还原