摘要
目前,针对薄壁件铣削加工过程中的颤振识别问题,普遍采用传感器信号进行判别与预测,而没有建立颤振特征与加工表面的相关联系。本文利用图像处理与模式识别技术,通过铣削表面图像实现薄壁件加工状态的精确辨识与预测。首先,设计了混合滤波方案,实现了采集图像的预处理;然后,通过改进的局部二值模式和灰度共生矩阵提取图像的颤振纹理特征,并以K近邻分类算法对铣削加工过程中采集的图像进行预测和识别。实验结果表明:该模型辨识的准确率为95.5%,算法平均运行时间为0.069 s。实验结果验证了该方法具有较高的辨识准确率,同时满足颤振预测及检测的实时性需求,对薄壁件铣削加工状态的识别及智能加工具有良好的指导意义。
At present,sensor signals are widely used to identify and predict the chatter in the milling process of thin-walled parts,but the correlation between the chatter characteristics and the machined surface is not established.In this paper,image processing and pattern recognition technology are used to accurately identify and predict the machining state of thin-walled parts through milling surface images.Firstly,a hybrid filtering scheme is designed to realize the preprocessing of the collected image,then the chatter texture features of the image are extracted through the improved local binary pattern and gray level co-occurrence matrix,and the images collected in the milling process are predicted and recognized by knearest neighbor classification algorithm.The experimental results show that the accuracy of the model identification is 95.5%and the average running time of the algorithm is 0.069 s.The experimental results show that the method has high identification accuracy,meets the real-time requirements of chatter prediction and detection,and has good guiding significance for milling state identification and intelligent machining of thin-walled parts.
作者
李茂月
刘硕
田帅
肖桂风
LI Mao-yue;LIU Shuo;TIAN Shuai;XIAO Gui-feng(Key Laboratory of Advanced Manufacturing and Intelligent Technology,Ministry of Education,Harbin University of Science and Technology,Harbin 150080,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2022年第2期425-432,共8页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(51975169)
黑龙江省普通高校基本科研业务费专项项目(2019-KYYWF-0204).
关键词
图像识别
铣削颤振
薄壁件
局部二值模式
混合滤波
image recognition
milling chatter
thin-walled parts
local binary pattern
hybrid filtering
