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针对数控铣削加工工艺的复杂性,以DMC60H为平台,以壳体类铝合金零件加工工艺数据为对象,提出了基于神经网络的数控铣削参数工艺效果预测方案。预测结果表明,所建立的工艺效果预测模型能够比较精确地预测出给定加工参数下的加工时间、尺寸精度和表面粗糙度,实现了数控铣削参数的合理选取,充分发挥了机床效能,提高了产品质量和生产效率,为生产实践提供了理论依据。
Aimed at the complexity of NC milling process, the DMC60H is taken as the platform and the process data of the shell-like aluminum alloy parts are taken as the object. The forecasting scheme of CNC milling parameters based on neural network is proposed. The prediction results show that the established process effect prediction model can predict the machining time, the dimensional accuracy and the surface roughness under a given machining parameter more accurately, and realize the reasonable selection of the numerical control milling parameters, fully exert the machine tool’s efficiency, Product quality and production efficiency, providing a theoretical basis for production practice.