Neural network based non-standard feature recognition to integrate CAD and CAM
Neural network based non-standard feature recognition to integrate CAD and CAM
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DOI：
10.1016/s0166-3615(01)00090-2
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发表时间：
2001-06-01
影响因子：
10
通讯作者：
Öztürk, F
中科院分区：
文献类型：
--
作者：
Öztürk, N;Öztürk, F
本文提出了一种基于神经网络的特征识别方法，该方法能够从设计数据库中提取信息，以自动集成设计和设计后的应用程序。将CAD数据库转换为CAM应用程序可以使用的基于特征的模型信息。多层感知器神经网络提供了边界表示（B-rep）的信息，以识别简单和复杂的功能。B-rep结构用于根据实体模型的几何和拓扑来处理面得分值。实验结果表明，该方法对复杂形状特征的识别是有效的。(C)2001 Elsevier Science B. V.保留所有权利。
In this paper, a neural network based feature recognition approach which is capable of extracting information from design database is proposed to automate the integration of the design and applications following design. CAD data base is converted to feature based model information which can be used by CAM applications. Multilayer perceptron neural network is provided with Boundary representation (B-rep) information to recognise simple and complex features. B-rep structure is used to process the face-score values in terms of geometry and topology of the solid model. The effectiveness of proposed approach is demonstrated with experimental results which show the validity of this method to recognise complex shape features. (C) 2001 Elsevier Science B.V. All rights reserved.