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

Abstract

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

CLC Number:

TH741

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.

Figures/Tables 12

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References 17

1	张雪薇, 于天彪, 王宛山. 薄壁零件铣削三维颤振稳定性建模与分析[J]. 东北大学学报: 自然科学版, 2015, 36(1): 99-103.
	Zhang Xue-wei, Yu Tian-biao, Wang Wan-shan. Modeling and analysis for 3D chatter stability of thin-walled parts in milling process[J]. Journal of Northeastern University(Natural Science), 2015, 36(1): 99-103.
2	王志学, 刘献礼, 李茂月, 等. 切削加工颤振智能监控技术[J]. 机械工程学报, 2020, 56(24): 1-23.
	Wang Zhi-xue, Liu Xian-li, Li Mao-yue, et al. Intelligent monitoring and control technology of cutting chatter[J]. Journal of Mechanical Engineering, 2020,56(24): 1-23.
3	Schmitz T. The microphone feedback analogy for chatter in machining[J/OL]. [2021-08-05].
4	Li L H, An Q B. An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis[J]. Measurement, 2016, 79(4): 44-52.
5	Lin Jian-gang, Wang Dong-xing, Tian Hong-zhi,et al. Surface defect detection of machined parts based on machining texture direction[J]. Measurement Science and Technology, 2021, 32(2): 1-12.
6	Szydlowski M, Powalka B. Chatter detection algorithm based on machine vision[J]. The International Journal of Advanced Manufacturing Technology, 2012, 62(5-8): 517-528.
7	Lei N, Soshi M. Vision-based system for chatter identification and process optimization in high-speed milling[J]. The International Journal of Advanced Manufacturing Technology, 2017, 89(9-12): 2757-2769.
8	Khalifa O, Densibali A, Faris W. Image processing for chatter identification in machining processes[J]. The International Journal of Advanced Manufacturing Technology, 2006, 31(5/6): 443-449.
9	冯东海. 基于计算机视觉的薄壁件铣削颤振在线监测[D]. 秦皇岛: 燕山大学机械工程学院, 2018.
	Feng Dong-hai. Chatter monitoring on-line of milling thin-walled parts based on computer vision[D]. Qinhuangdao: College of Mechanical Engineering, Yanshan University, 2018.
10	林洁琼, 周晓勤, 孔繁森, 等. 再生切削颤振系统动态响应谐参数辨识[J]. 吉林大学学报: 工学版, 2009, 39(4): 964-969.
	Lin Jie-qiong, Zhou Xiao-qin, Kong Fan-sen, et al. Harmonic parameter identification of dynamic response in regenerative machining chatter system[J]. Journal of Jilin University(Engineering and Technology Edition), 2009, 39(4): 964-969.
11	李秀怡. 图像纹理检测与特征提取技术研究综述[J]. 中国管理信息化, 2017, 20(23): 175-178.
	Li Xiu-yi. Review of image texture detection and feature extraction[J]. China Management Information, 2017, 20(23): 175-178.
12	Ojala T, pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987.
13	戴金波, 肖霄, 赵宏伟. 基于低分辨率局部二值模式的人脸识别[J]. 吉林大学学报: 工学版, 2013, 43(2): 435-438.
	Dai Jin-bo, Xiao Xiao, Zhao Hong-wei. Human face recognition based on low resolution local binary pattern[J]. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(2): 435-438.
14	Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, 3(6): 610-621.
15	鲁恒润, 王卫东, 徐志强, 等. 基于机器视觉的煤矸特征提取与分类研究[J]. 煤炭工程, 2018, 50(8): 137-140.
	Lu Heng-run, Wang Wei-dong, Xu Zhi-qiang, et al. Research on feature extraction and classification of coal gangue based on machine vision[J]. Coal Engineering, 2018, 50(8): 137-140.
16	Mou Wen-ping, Zhu Shao-wei, Jiang Zhen-xi, et al.Vibration signal-based chatter identification for milling of thin-walled structure[J]. Chinese Journal of Aeronautics, 2020, 35(1): 204-214.
17	孙艺珊, 李晓洁, 赵凯. 改进LBP和HSV颜色直方图相结合的地表状态识别[J]. 测绘通报, 2020(2): 29-36, 71.
	Sun Yi-shan, Li Xiao-jie, Zhao Kai. A combined algorithm of improved LBP and HSV for surface state recognition[J]. Bulletin of Surveying and Mapping, 2020(2): 29-36, 71.

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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

Image feature extraction and recognition of milling chatter of thin walled parts

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