摘要
针对目前网络化制造环境下设备分组和检索算法效率不高的现状,提出了基于设备加工特征向量的分组方法来提高算法的效率。首先,按照设备所能完成的加工特征,从特征的形状、尺寸以及加工精度等三个维度构建了制造设备的加工特征描述向量;然后基于制造设备的加工特征描述向量,通过设计了初始分组中心选择策略和采用非遍历分组组数优化技术,改进了扩展模糊C-均值算法,构建了基于加工特征向量的设备分组模糊聚类算法。该算法可高效、精确地计算出设备最优分组组数。最后,以50台制造设备的分组为例验证了该算法的有效性。
Aiming at improving the efficiency of the algorithm for fast grouping and retrieval of manufacturing equipment, a notion of Manufacturing Feature Vector （MFV） based equipment description was proposed to improve the efficiency of the algorithm. First,general manufacturing features were induced and further classified according to the principles of feature classification designed on the geometry type of manufacturing feature. The combination of the manufacturing features which could be machined by the equipment was defined as the MFV of the device. Type, size and precision were Used as the three attributes of the MFV. Then, extended fuzzy C-means clustering algorithm was amend to fulfill the task of manufacturing equipment grouping based on MFC. The algorithm could efficiently and accurately calculate the range of the optimal grouping number. Finally, a case of 50 manufacturing equipment grouping was used to verify the effectiveness of the algorithm.
出处
《现代制造工程》
CSCD
北大核心
2016年第1期1-6,72,共7页
Modern Manufacturing Engineering
基金
国家部委基金资助项目(VTDP-1501
EDP1901)
国家自然科学基金资助项目(51275049)
关键词
模糊C-均值聚类
加工特征向量
设备分组
fuzzy C-means clustering
manufacturing feature vector
equipment grouping
