Segmentation and evaluation of diamond abrasive grains based on K-Means clustering and convex hull detection

LI Hongyang; FANG Congfu

doi:10.13394/j.cnki.jgszz.2022.0099

Volume 43 Issue 2

Apr. 2023

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LI Hongyang, FANG Congfu. Segmentation and evaluation of diamond abrasive grains based on K-Means clustering and convex hull detection[J]. Diamond & Abrasives Engineering, 2023, 43(2): 188-195. doi: 10.13394/j.cnki.jgszz.2022.0099

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LI Hongyang, FANG Congfu. Segmentation and evaluation of diamond abrasive grains based on K-Means clustering and convex hull detection[J]. Diamond & Abrasives Engineering, 2023, 43(2): 188-195. doi: 10.13394/j.cnki.jgszz.2022.0099

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Segmentation and evaluation of diamond abrasive grains based on K-Means clustering and convex hull detection

doi: 10.13394/j.cnki.jgszz.2022.0099

LI Hongyang,
FANG Congfu^,

College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, Fujian , China

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Received Date: 2022-06-24
Accepted Date: 2022-09-14
Rev Recd Date: 2022-08-17

Abstract

Abstract

Diamond tools are widely used in grinding, wire sawing and other fields. The characteristics of abrasive particles on the surface are an important factor affecting the machining results and tool performance. To process abrasive grain images with complex background information, this paper proposed an abrasive grain segmentation method based on K-means clustering and convex hull detection, which combines binarization, morphological processing, main contour extraction and other related operations to achieve abrasive grain extraction. Finally, three related indicators, including abrasive grain contour area accuracy η_CAA, abrasive grain position error θ_PE, and abrasive grain quantity recall rate σ_QR, were proposed to evaluate the segmentation effect. The results show that the average contour area precision is 98.30%, the average position error is only 2.93%, and the average number recall rate is 95.91%, which proves the accuracy of the method.
- diamond tools,
- abrasive grain characteristics,
- K-means clustering,
- convex hull detection ; abrasive grain segmentation

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References(17)

References

[1]	ZHANG Y, FANG C, HUANG G, et al. Modeling and simulation of the distribution of undeformed chip thicknesses in surface grinding [J]. International Journal of Machine Tools and Manufacture,2018,127:14-27. doi: 10.1016/j.ijmachtools.2018.01.002
[2]	BI G, ZHENG S, ZHOU L. Online monitoring of diamond grinding wheel wear based on linear discriminant analysis [J]. The International Journal of Advanced Manufacturing Technology,2021,115(78):2111-2124.
[3]	张秀芳, 于爱兵, 贾大为, 等. 应用数字图像识别法检测金刚石磨粒的形状与粒度 [J]. 金刚石与磨料磨具工程,2007(1):47-49. doi: 10.3969/j.issn.1006-852X.2007.01.012 ZHANG Xiufang, YU Aibing, JIA Dawei, et al. Measuring the shape and size of diamond grains by digital image identification method [J]. Diamond & Abrasives Engineering,2007(1):47-49. doi: 10.3969/j.issn.1006-852X.2007.01.012
[4]	李银华, 路新惠, 靳贺敏. 基于图像处理的金刚石磨粒体积计算研究 [J]. 计算机工程与设计,2009,30(18):4242-4244. doi: 10.16208/j.issn1000-7024.2009.18.001 LI Yinhua, LU Xinhui, JIN Hemin. Calculation of volume for diamond grains based on image processing [J]. Computer Engineering and Design,2009,30(18):4242-4244. doi: 10.16208/j.issn1000-7024.2009.18.001
[5]	吴文艺, 崔长彩, 叶瑞芳, 等. 采用二次灰度直方图的砂轮磨粒图像阈值分割[J]. 华侨大学学报(自然科学版). 2016, 37(4): 422-426. WU Wenyi, CUI Changcai, YE Ruifang, et al. Image segmentation method using second time gray level histogram of connected component labeling of grinding wheel abrasives grains [J]. Journal of Huaqiao University (Natural Science Edition), 2016, 37(4): 422-426.
[6]	杨栖凤, 崔长彩, 黄国钦. 金刚石砂轮表面二维形貌全场测量和分析 [J]. 华侨大学学报(自然科学版),2018,39(4):479-484. YANG Qifeng, CUI Changcai, HUANG Guoqin. Measurement and analysis of two-dimensional surface topography of whole grinding wheel [J]. Journal of Huaqiao University (Natural Science Edition),2018,39(4):479-484.
[7]	潘秉锁, 潘文超, 刘子玉. 基于空洞卷积神经网络的金刚石图像语义分割 [J]. 金刚石与磨料磨具工程,2019,39(6):20-24. doi: 10.13394/j.cnki.jgszz.2019.6.0004 PAN Bingsuo, PAN Wenchao, LIU Ziyu. Semantic segmentation of diamond images based on hollow convolutional neural networks [J]. Diamond & Abrasives Engineering,2019,39(6):20-24. doi: 10.13394/j.cnki.jgszz.2019.6.0004
[8]	LIN Y, FANG C, DENG Y. Segmentation and extraction for the diamond grain image based on the gauss-polymerized enhancement [J]. Journal of physics: Conference Series, 2019,1169(1):12042.
[9]	LIN Y, FANG C. Study on the segmentation of abrasive grains in diamond tools [J]. International Journal of Abrasive Technology,2018,3(8):203-217.
[10]	PAN B, YANG Y, ZHANG Y. Extraction of diamond grain topography from diamond tool surface using 3D surface measurement coupled with image analysis [J]. Measurement,2019,133:9-13. doi: 10.1016/j.measurement.2018.10.003
[11]	赵玉康, 毕文波, 葛培琪. 电镀金刚石线锯表面磨粒分布密度的多相机视觉检测 [J]. 金刚石与磨料磨具工程,2021,41(2):64-68. doi: 10.13394/j.cnki.jgszz.2021.2.0011 ZHAO Yukang, BI Wenbo, GE Peiqi. Multi-camera visual inspection of abrasives distribution density on electroplated diamond wire saw surface [J]. Diamond & Abrasives Engineering,2021,41(2):64-68. doi: 10.13394/j.cnki.jgszz.2021.2.0011
[12]	KANG M, ZHANG L, TANG W. Study on three-dimensional topography modeling of the grinding wheel with image processing techniques [J]. International Journal of Mechanical Sciences,2020,167:105241. doi: 10.1016/j.ijmecsci.2019.105241
[13]	赵文昌, 李忠木. 融合改进人工蜂群和K均值聚类的图像分割 [J]. 液晶与显示,2017,32(9):726-735. doi: 10.3788/YJYXS20173209.0726 ZHAO Wenchang, LI Zhongmu. Image segmentation algorithm based on improved artificial bee colony and K-mean clustering [J]. Liquid Crystal and Display,2017,32(9):726-735. doi: 10.3788/YJYXS20173209.0726
[14]	段明义, 卢印举, 张文. 一种改进的舰船合成孔径雷达图像分割方法 [J]. 太赫兹科学与电子信息学报, 2021, 19(5): 905-909. DUAN Mingyi, LU Yinju, ZHANG Wen. An improved ship synthetic aperture radar image segmentation method [J]. Journal of Terahertz Science and Electronic Information, 2021, 19(5): 905-909.
[15]	彭金喜, 苏远歧, 薛笑荣. 一种小波域K-Means遥感图像分类标注算法 [J]. 软件导刊,2019,18(9):202-206. PENG Jinxi, SU Yuanqi, XUE Xiaorong. A remote sensing image semantic classification label of K-means clustering on wavelet transform [J]. Software Guide,2019,18(9):202-206.
[16]	李冰, 何超. 基于背景骨架特征的粘连米粒图像分割算法 [J]. 计算机应用,2017,37(S2):198-202. LI Bing, HE Chao. Segmentation algorithm of touching rice kernels based on skeleton features of image background [J]. Computer Applications,2017,37(S2):198-202.
[17]	吴忻生, 刘洋, 戚其丰. 基于凹性分析的粘连车辆分割 [J]. 计算机应用研究,2012,29(1):344-347. doi: 10.3969/j.issn.1001-3695.2012.01.095 WU Xinsheng, LIU Yang, QI Qifeng. Adhered vehicles segmentation based on concavity analysis [J]. Computer Application Research,2012,29(1):344-347. doi: 10.3969/j.issn.1001-3695.2012.01.095

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Segmentation and evaluation of diamond abrasive grains based on K-Means clustering and convex hull detection

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