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航空发动机是一个国家工业发展水平的重要体现。对比于一般的机械加工制造,航空零部件对机械加工的精度要求更高,避免因加工质量缺陷而影响航空发动机的使用质量和使用寿命。在航空发动机机械零部件的加工中尤其是在对叶片进行加工时为避免加工工序出现偏差,需要对发动机叶片的加工工序进行严格的测算。文章在分析工时定额特性的基础上提出了基于SLFM神经网络模型的航空发动机叶片工时定额计算新方法,从而实现对于航空发动机叶片工时定额的快速估算.
Aero-engine is an important manifestation of a country’s industrial development level. Compared with the general machining manufacturing, aviation components require more precision machining, to avoid processing quality defects affect the quality and service life of the aircraft engine. In the machining of aeroengine machinery parts, especially in the processing of the blade in order to avoid the deviation of the machining process, it is necessary to make rigorous calculations on the machining process of the engine blade. In this paper, a new method for calculating the working hours and quotas of aeroengine blades based on the SLFM neural network model is proposed based on the analysis of the quotas of working hours, so as to realize the rapid estimation of working hours and quotas of aeroengine blades.