薄壁件铣削加工颤振的图像特征提取与识别

doi:10.13229/j.cnki.jdxbgxb20211116

摘要/Abstract

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。

关键词: 图像识别, 铣削颤振, 薄壁件, 局部二值模式, 混合滤波

Abstract:

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 k-nearest 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.

Key words: image recognition, milling chatter, thin-walled parts, local binary pattern, hybrid filtering

中图分类号:

TH741

李茂月,刘硕,田帅,肖桂风. 薄壁件铣削加工颤振的图像特征提取与识别[J]. 吉林大学学报(工学版), 2022, 52(2): 425-432.

Mao-yue LI,Shuo LIU,Shuai TIAN,Gui-feng XIAO. Image feature extraction and recognition of milling chatter of thin walled parts[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(2): 425-432.

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参考文献 17

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加工状态	ASM均值	ENT均值	CON均值	IDM均值	ASM标准差	ENT标准差	CON标准差	IDM标准差
稳定阶段	0.590 42	0.921 04	0.168 16	0.916 01	0.012 64	0.023 87	0.018 10	0.029 32
过渡阶段	0.245 85	1.775 20	0.243 53	0.879 02	0.014 14	0.051 86	0.034 92	0.051 38
颤振阶段	0.091 19	2.712 51	0.468 25	0.893 72	0.028 98	0.089 10	0.088 09	0.028 98

算法	特征提取时间/s	模式识别时间/s
原始LBP	0.031	0.026
改进LBP	0.035	0.024
改进LBP+GLCM	0.040	0.029