Identification of Tool Wear Condition Based on Generalized Fractal Dimensions and BP Neural Network Optimized with Genetic Algorithm

摘要：

　　Based on multi-fractal theory, the generalized fractal dimensions of acoustic emission (AE) signals during cutting process were calculated using improved box-counting method.The generalized dimension spectrums of AE signals for different tool wear condition were gained, and the relation between tool wear condition and generalized dimensions was analyzed.Together with cutting process parameters, the generalized fractal dimensions were taken as the input vectors of BP neural network after normalization.The initial weight and bias values of BP neural network which was used to classify the tool wear condition were optimized with Genetic Algorithm.The test results showed that the method can be used effectively for the identification of tool wear condition.

关键词： generalized fractal dimension BP neural network tool wear condition identification Genetic Algorithm

作者: Kai-feng Zhang Hui-qun Yuan Peng Nie

作者单位: School of Mechanical Engineering and Automation, Northeastern University,Shenyang, China;School of M School of Mechanical Engineering and Automation, Northeastern University,Shenyang, China School of Mechanical and Electrical Engineering, Shenyang Aerospace University,Shenyang, China

会议类型: 国际会议

会议名称: 2013 International Conference on Machinery,Materials Science and Energy Engineering(2013机械、材料科学与能源工程国际会议)(ICMMSEE2013)

会议地点: 湖北荆州

会议语种:英文

页码: 71-75

在线出版日期: 2013-05-18（万方平台首次上网日期，不代表论文的发表时间）