ABSTRACT

The implementation of automatic feature recognition (AFR) techniques is considered an indispensable concept in transferring product data between computer-aided design (CAD) and computer-aided process planning (CAPP). Different AFR techniques and systems have been developed to serve this aim; however, each of them have limitations. The main research gap is that each system is restricted to a specific set of predefined manufacturing features, which makes the universality of these methods difficult to extended. To solve this problem, a new hybrid 3D feature recognition method (graph and rule based) is proposed for recognizing machining features, and shaft parts are taken as an example in this paper. First, the reverse modeling method is used to classify the machining features in the part design process. Second, the 3D model is represented by B-Rep, and the weighted attribute adjacency matrix (WAAM) is proposed to represent the data structure of the B-Rep model. Third, the recognition and suppression rules are defined. Finally, three typical shaft parts are used as the test cases in MATLAB. The test results show hybrid feature recognition method can recognize all features. The comparative test shows that the practicability and efficiency of the method are satisfactory.

抽象的

在计算机辅助设计（CAD）和计算机辅助过程计划（CAPP）之间传输产品数据时，自动功能识别（AFR）技术的实施被认为是必不可少的概念。为了达到这个目的，已经开发了不同的AFR技术和系统。但是，它们每个都有局限性。主要的研究差距是每个系统都限于一组特定的预定义制造特征，这使得这些方法的通用性难以扩展。为解决这一问题，提出了一种新的基于图形和规则的混合3D特征识别方法，用于识别加工特征，并以轴零件为例。首先，使用反向建模方法对零件设计过程中的加工特征进行分类。其次，以B-Rep代表3D模型，提出了加权属性邻接矩阵（WAAM）来表示B-Rep模型的数据结构。第三，定义识别和抑制规则。最后，在MATLAB中将三个典型的轴零件用作测试用例。测试结果表明，混合特征识别方法可以识别所有特征。对比试验表明，该方法的实用性和有效性令人满意。