模糊数据融合的刀具磨损状态智能识别  

Fuzzy Data Fusion for Recognition of Tool Wear

作　　者：刘建萍[1] 叶邦彦[2] 

机构地区：[1]佛山职业技术学院,广东佛山528237 [2]华南理工大学机械与汽车工程学院,广东广州510640

出　　处：《机械与电子》2010年第4期49-53,共5页Machinery & Electronics

基　　金：国家自然科学基金资助项目(50875089)

摘　　要：为实现高速加工时刀具渐变磨损状态的在线准确识别,提出了一种集合多种智能的间接检测刀具磨损状态方法的模糊数据融合方法.尽管这些方法具有算法实现较为简单、处理速度较快的优点,但单一的信号检测及单一的智能建模方法难以获得全面的加工状态信息和准确的识别结果.为此,利用F推理技术对上述方法的冗余和互补信息进行数据融合,应用Makino-Fanuc 74-A20型加工中心的测试数据验证了该方案的可行性,并将刀具后刀面磨损的预测值与基于机器视觉检测的实测值进行比较.实验结果分析表明,多参数模糊融合识别方法能快速获得切削刀具磨损状态更加准确的预测值.For detecting gradual tool wear state on line in high cutting speed,the methods of wave- let fuzzy neural network9 regression neural network and sample classification fuzzy neural network by detecting cutting force, motor power of machine tool and AE signal respectively are presented. A1- though these methods are not difficult to come true and processed accurately and rapidly,it is difficult to obtain comprehensive information of machining and exact value of tool wear when using single method of intelligent modeling and single signal detecting. For this purpose, fuzzy inference technique is adopted to fuse the recognized data. Emu-lation experiment is carried out by using Matlab software platform and this method is verified to be feasible. Experimental result indicates that by applying fuzzy data fusion, we can get an exact tool wear forecast rapidly.

关 键 词：刀具磨损状态识别 模糊数据融合 神经网络 模糊聚类 小波分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]