同步器齿毂是汽车变速器装置的重要零件,其加工质量对变速器的性能、可靠性有直接影响.针对人工经验判断齿毂误差源范围效率较低的问题,本文提出一种基于蝙蝠算法优化 BP 神经网络的误差溯源方法,分析齿毂加工过程中的误差来源,利用蝙蝠算法对权值和阈值进行优化,获取最优值后构造BA-BP误差溯源模型,并采集数据样本对模型进行验证并与未优化之前的BP神经网络的误差溯源方法进行对比.与未优化之前 BP 神经网络溯源模型准确率 83.56%相比,优化后的准确率为 96.34%,该方法使溯源准确率明显提高,支持生产人员对后续的超差工件进行误差原因追溯,对生产过程中存在的问题直接进行处理排除,提高生产效率.
Tooth hub error tracing of automobile synchronizer based on BA-BP
As an important part of automobile transmission device,the machining quality of synchronizer tooth hub has a direct impact on the performance and reliability of the transmission.Aiming at the problem of low efficiency in judging the range of tooth hub error source by manual experience,this paper proposed an error tracing method based on bat algorithm to optimize BP neural network.The error sources in the tooth hub machining process were analyzed,and the bat algorithm was used to optimize the weights and thresholds.The BA-BP error tracing model was constructed after obtaining the optimal value,data samples were collected to verify the model and compared with the error traceability method of BP neural network before optimization.Compared with the accuracy of the BP neural network traceability model before the optimization was 83.56%,the optimized accuracy was 96.34%,which significantly improved the traceability accuracy,this method allows the production personnel to trace the error causes of the subsequent out-of-tolerance workpieces,which is convenient to directly deal with and eliminate the problems in the production process,so as to improve the production efficiency.
万方数据
维普
