大型装备制造企业期量标准智能生成系统研究与实践

大型装备制造企业产品规模大、结构复杂,通常按订单生产,产品变型设计频繁,造成产品期量标准的制定非常复杂,准确性差,大大影响了ERP的实施效果。针对该问题,提出了期量标准的智能化解决方案。利用BP神经网络及其变形网络"识别"历史数据中最相似的"零件模型",对新型零件的提前期进行"预测"。在此基础上提出了详细设计方案,开发出了相应的计算机系统,运用BP神经网络结合梯度下降法对变型零件的期量标准进行估算。

A Research and Practice on the Generation System of Period and Quantity Standard in Large-scale Equipment Manufacturing Enterprises

The large-scale production and the variety of complex orders make it necessary to frequently redesign the types of products,which adds to the complexity of the generation of period quantity standards.The imprecise data also affect the application of ERP.And intelligent solution to the problem of period and quantity standards was introduced.It utilized the BP neural network and its transmutation network to identify the nearest part model in the history data,and to estimate the lead time of the redesigned parts.Based on this theory,a detailed design proposal was put forward,and corresponding compute programs were developed.The BP neural network and gradient decent method were used to estimate the period and quantity standards of the redesigned parts.