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首页> 外文期刊>Journal of Intelligent Manufacturing >Manufacturing features recognition using backpropagation neural networks
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Manufacturing features recognition using backpropagation neural networks

机译：制造功能识别使用BackPropagation神经网络

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A backpropagation neural network (BPN) is applied to the problem of feature recognition from a boundary representation (B-rep) solid model to facilitate process planning of manufactured products. It is based on the use of the face complexity code to represent the features and a neural network for the analysis of the recognition. The face complexity code is a measure of the face complexity of a feature based on the convexity or concavity of the surrounding geometry. The codes for various features are fed to the network for analysis. A backpropagation network is implemented for recognition of features and tested on published results to measure its performance. Any two or more features having significant differences in face complexity codes were used as exemplars for training the network. A new feature presented to the network is associated with one of the existing clusters, if they are similar, or the network creates a new cluster, if otherwise. Experimental results show that the network was consistent in recognizing features, hence is appropriate for application to the problem of feature recognition in automated manufacturing environment.
机译：从边界表示（B-REP）实体模型的特征识别问题应用了反向桥断神经网络（BPN），以便于制造产品的过程规划。它基于面部复杂性代码的使用来表示用于分析识别的特征和神经网络。面部复杂性代码是基于周围几何形状的凸起或凹面的特征的面部复杂度的度量。各种特征的代码被馈送到网络以进行分析。对BackProjagation网络实现以识别功能并在发布的结果上测试以测量其性能。使用具有显着差异的两个或更多个特征，脸部复杂性代码被用作训练网络的示例。呈现给网络的新功能与现有群集之一相关联，如果它们相似，或者如果否则，网络会创建新群集。实验结果表明，该网络在识别特征中是一致的，因此适用于应用于自动化制造环境中的特征识别问题。

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