[1]李明洋,唐国宝,李乾,等.基于KNN算法的铝合金阳极化层打磨质量检测系统研究[J].机械与电子,2018,(06):45-49.
　LI Mingyang,TANG Guobao,LI Qian,et al.Research on Quality Detection System for Aluminum Alloy Anodizing Layer Polishing Based on KNN Algorithm[J].Machinery & Electronics,2018,(06):45-49.
点击复制

基于KNN算法的铝合金阳极化层打磨质量检测系统研究()
分享到：

机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年06期
页码:
45-49
栏目:
自动控制与检测
出版日期:
2018-06-24

文章信息/Info

Title:
Research on Quality Detection System for Aluminum Alloy Anodizing Layer Polishing Based on KNN Algorithm
文章编号:
1001-2257（2018）06-0045-05
作者:
李明洋1; 唐国宝2; 李乾1; 刘尔彬2; 高永卓1
(1.哈尔滨工业大学机器人系统与技术国家重点实验室,黑龙江 哈尔滨 150080;2.广州瑞松智能科技股份有限公司,广东 广州 510760)
Author(s):
LI Mingyang1;  TANG Guobao2;  LI Qian1;  LIU Erbin2;  GAO Yongzhuo1
(1. State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China; 2.Guangzhou Risong Technology Co., Ltd., Guangzhou 510760, China)
关键词:
KNN分类算法; 机器人; 阳极化层打磨; 质量检测
Keywords:
KNN classification algorithm;  robot;  anodizing layer polish;  quality inspection
分类号:
TP274.5
文献标志码:
A
摘要:
结合航空航天行业对铝合金焊接的要求，提出了一种基于KNN算法的铝合金工件阳极化层打磨质量视觉检测系统。系统利用SCARA机器人及固定在其上的图像获取装置多角度、多方位、多视场的对传送设备上的工件进行拍摄，并通过图像处理技术得出工件的打磨质量检测结论。结合铝合金特点，利用打磨前后工件颜色不同，采用KNN分类算法对工件打磨区域进行分类，得到工件被打磨区域宽度的测量值以及工件打磨的质量，并与采用传统动态阈值分割、边缘提取等图像处理方法进行了对比。最终完成铝合金工件阳极化层打磨质量检测实验，结果表明，系统可以有效地对打磨质量进行检测，并且与其他方法相比有较高的稳定性。
Abstract:
In order to meet the requirements of aerospace industry for welding of aluminum alloys, this paper proposes a visual inspection system for the quality of anodized layer of aluminum alloy workpieces based on KNN algorithm.?Firstly, the system used the SCARA robot and the image acquisition device fixed on it to photograph the workpieces on the conveyor equipment from multi-angle, multi-azimuth, and multi-view, and then, it applied the image processing technology to draw conclusions on the quality of the grinding. Next, in combination with the characteristics of aluminum alloys and different colors of workpieces before and after grinding, the KNN classification algorithm was used to classify the grinding areas so as to obtain the measured value of the width of the grinding area as well as the quality of grinding, which were compared with those results acquired by using conventional image processing methods. Finally, the quality test of the anodizing layer of aluminum alloy workpieces was completed. The results show that the system can effectively detect the quality of grinding, and has higher stability compared with other methods

参考文献/References:

[1]Warner T．Recently-developed aluminium solutions for aerospace applications[J]．Materials Science Forum2006，519/520/521:1271-1278.
[2]张新明，刘胜胆．航空铝合金及其材料加工[J]．中国材料进展，2013，32(1):39-55．
[3]白嘉远．铝合金材料的应用及其加工成形技术[J]．世界有色金属，2017(14):272-273．
[4]杨萍，赵丹，李鹤岐，等．专用焊接打磨机器人视觉系统的研究[J]．兰州理工大学学报，2004，30(3):41-44．
[5]刘东升．基于双目视觉的机器人在线检测技术的研究[D]．青岛:青岛科技大学，2013．
[6]王峰．机器视觉钢轨轮廓打磨诊断系统关键技术研究[D]．武汉：武汉理工大学，2014．
[7]黄晔．基于工业机器人的X射线CT检测系统研究[D]．北京：北京理工大学，2015．
[8]卢军，郑国穗，马金锋，等．SCARA机器人结构优化设计与运动分析[J]．陕西科技大学学报(自然科学版)，2014，32(1)：137-141．
[9]Liu H，Zhang S．Noisy data elimination using mutual k-nearest neighbor for classification mining[J]．Journal of Systems Software，2012，85 (5)：1067-1074.
[10]Cheng H，Yu R，Liu Z，．Kernelized pyramid nearest-neighbor search for object categorization [J]．Machine Vision and Applications2014，25(4)：931-941.
[11]Xu F， Yang Z， Jiang G． An analyzing and experimental method based on the resultant motion signals for SCARA manipulator joints [J]．High Technology Letters，2017，23(3):279-285．
[12] Gu J，Pan Y，Wang H．Research on the improvement of image edge detection algorithm based on artificial neural network[J]．Optik - International Journal for Light and Electron Optics，2015，126(21)
[13]Crosier M， Griffin L D．Using basic image features for texture classification[J]．International Journal of Computer Vision2010 ,88(3)：447-460.

相似文献/References:

[1]胡 聪１,凌 烈２,３,等.面向悬垂绝缘子串更换任务的机器人机构设计与优化[J].机械与电子,2019,(09):75.
　,,et al.Design and Optimization of Robot Mechanism for Suspension Insulator String Replacement Task[J].Machinery & Electronics,2019,(06):75.
[2]张 程１,２,王伟栋１,等.机器人碰撞保护[J].机械与电子,2019,(08):76.
　,,et al.Robot Collision Protection[J].Machinery & Electronics,2019,(06):76.
[3]杨青丰１,２,冯宝林１,等.基于机械臂灵巧手智能数据采集系统的设计与分析[J].机械与电子,2019,(07):75.
　,,et al.Design and Analysis of Intelligent Data Acquisition System Based on Robotic Hand[J].Machinery & Electronics,2019,(06):75.
[4]谢智慧,卢道华,王 佳,等.基于改进蚁群算法的机器人路径规划问题研究[J].机械与电子,2019,(06):70.
　,,et al.Research on Robot Path Planning Problem Based on Improved Ant Colony Algorithm[J].Machinery & Electronics,2019,(06):70.
[5]达悦生,郑楚悦,孙茂荣.机器人运动学模型建立的改进 ＤＨ 方法及正反解计算[J].机械与电子,2019,(10):72.
　, Improved DH Method and Forward and Inverse Solution Calculation for Robot Kinematics Model Establishment[J].Machinery & Electronics,2019,(06):72.
[6]向真,张宏钊,姜勇,等.一种高压室消防机器人[J].机械与电子,2018,(12):64.
　XIANG Zhen,ZHANG Hongzhao,JIANG Yong,et al.A High-voltage Distribution Room Firefighting Robot System[J].Machinery & Electronics,2018,(06):64.
[7]夏 康,黄晓华,周俊俊,等.管道机器人跨井机构的设计与研究[J].机械与电子,2018,(08):71.
　XIA Kang,HUANG Xiaohua,ZHOU Junjun,et al.Design and Research of Spanning Mechanism of Pipeline Robot[J].Machinery & Electronics,2018,(06):71.
[8]张超,周自强,谭翰墨,等.基于PC-Interface的机器人拆卸操作仿真研究[J].机械与电子,2018,(03):65.
　ZHANG Chao,ZHOU Ziqiang,TAN Hanmo,et al.Simulation of Robotic Disassembly Operation Based on PC-Interface[J].Machinery & Electronics,2018,(06):65.
[9]王 雄,高海艳,张 菁,等.轮腿式复合机器人设计及运动实现[J].机械与电子,2016,(01):72.
　WANG Xiong,GAO Haiyan,ZHANG Jing,et al.Design and Motion Realization of Wheel-legged Mobile Robot[J].Machinery & Electronics,2016,(06):72.
[10]刘大龙,余有明.仿生直立双足机器人的稳定性控制算法[J].机械与电子,2016,(02):76.
　LIU Dalong,YU Youming.Stability Control Algorithm of Bionic Upright Biped Robot[J].Machinery & Electronics,2016,(06):76.

备注/Memo

备注/Memo:
收稿日期：2018-01-23
基金项目：广东省重大科技专项项目（2015B010918001）
作者简介：李明洋（1994-），男，黑龙江哈尔滨人，博士研究生，主要研究方向为机器人技术。
更新日期/Last Update: 2019-10-30