回转类零件的人工神经网络工序选择方法研究

Research on ANN-Based Machining Operation Selection for Rotational Parts

  • 摘要: 分析了当前计算机辅助工艺设计(CAPP)的发展趋势,及CAPP专家系统存在的问题,提出了一种用人工神经网络技术解决CAPP中工序选择问题的方法。该方法以回转类零件为研究对象,结合成组技术,构建了一种由多个独立的人工神经网络构成的综合系统,提出了零件几何特征及其尺寸、公差、粗糙度分段编码作为神经网络的输入,所选择工序的代码作为输出,搜集样本与样本设计相结合的模式,有效地解决了神经网络应用中的关键技术问题。

     

    Abstract: A method of using artificial neural networks for the working procedure selection in CAPP is proposed in this paper based on the trends of CAPP and the existing problems in CAPP expert system. By taking rotational parts as the object and combining with group technology, an integrated system consisting of many independent artificial neural networks is established. With the subsection codes of the geometric features, dimensions, tolerances, and roughness as the input, the codes of the working procedure selections as the output, the key technical problems applying neural network in working procedure seletion can effectively solved.

     

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