基于聚类方法的CAPP零件知识库构建

传统的零件分类一般根据零件编码从特征矩阵中得到分类结果，未能很好地表达各个零件之间的相似关系，对工艺设计也不能提供启发性的推理策略．此外，零件分类矩阵本身的相似性标准也难以确定，给零件的工艺制作带来了很多困难．为此，提出了一种利用聚类技术构造树型结构表达零件相似性的方法，并根据零件之间的相似性建立层次结构以进行动态分类，进而构建一种有自学习能力的零件知识库．考虑到机器智能的局限性．分类结果可能不尽合理，分类树又能够在自动压缩优化的基础上进行手工优化，并将优化结果记录于分类树中．该知识库能及时反映零件信息的动态更新，并对零件进行多层次、细粒度的动态分类，使零件分类粒度不受数据规模的限制，从而可以通过建立索引结构，实现自适应的工艺设计自动化。

A Clustering Based Method for the Knowledge Base Construction of CAPP

Traditional parts classification results are acquired from feature matrices according to their coding. However, the method cannot express well the degree of similarity among the parts or provide a heuristic reasoning scheme for process design; furthermore, the similarity criteria themselves of the matrices are hard to determine. These bring many difficulties to the manufacturing process of the parts. This article proposes a method of describing similarity using clustering analysis, builds a hierarchical structure for the feature similarities among the parts to facilitate dynamic clustering and automatic process making, and makes a thorough study of the problem of parts clustering. Thus a knowledge base with self-learning ability is constructed, which may arrange parts more suitably, represents their inherent relationship of features and functions more accurately, and reflect immediate updating of parts.