Technology Analysis and Device Design for Automobile Wheel Hub Surface Defect Detection
LIU Fucai1,2,ZHANG Zhenyu1,XU Jilong1,2,ZHENG Hongwei1,LIU Yang3
1. Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Hebei High-end Equipment Industry Technology Research Institute, Qinhuangdao, Hebei 066004, China
3. CITIC Dicastal Co., Ltd., Qinhuangdao, Hebei 066004, China
Abstract:Machine vision, as an important method to replace manual detection of wheel hub surface defects, is currently the main research direction in this field. Therefore, a summary and analysis of the research status of surface defect detection technology for automotive hubs is conducted. Firstly, starting with the categories of wheel surface defects and manual detection process, the requirements and difficulties of machine vision-based wheel surface defect detection technology are expounded. Then, the development of intelligent detection algorithms based on machine vision is analyzed, including the application of traditional machine vision methods in defect image preprocessing, defect location and feature extraction, defect classification and recognition, also including the application of deep learning methods such as convolutional neural networks (CNN) in defect detection, segmentation, and other applications. Finally, the existing hub type recognition device, hub defect X-ray image acquisition device, hub surface defect image acquisition device are introduced. On the basis of analyzing the limitations and key technical issues that need to be solved in practical applications of current machine vision based intelligent detection devices, three kinds of intelligent detection experimental device design schemes are proposed. It provides theoretical and technical support for the development and performance improvement of automatic rapid detection device.
SHI M, QIAO K L, WANG S Q, et al. Research on defect detection method of sealing ring based on semantic segmentation[J]. High Technology Letters, 2021, 31(12): 1239-1247.
[5]
STRECKER H. Scatter Imaging of Aluminum Castings Using an X-Ray Fan Beam and A Pinhole Camera[J]. Materials Evaluation, 1982, 40(10): 1050-1056.
[7]
MERY D, JAEGER T, FILBERT D. A review of methods for automated recognition of casting defects[J]. Insight: Non Destructive Testing & Condition Monitoring, 2002, 44(7): 428-436.
[8]
MERY D, FILBERT D. Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence[J]. IEEE Transactions on Robotics and Automation, 2003, 18(6): 890-901.
[10]
CARRASCO M, MERY D. Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels[J]. Machine Vision and Applications, 2011, 22(1): 157-170.
ZHANG L Y, SUN X Y. Application and Research of X-Ray Real Time Imaging Technology in Automobile Parts Inspection[J]. Automobile Technology & Material, 2004(7): 105-107.
LU B. X-ray inspection of wheel production[J]. Auto Manufacturing Engineer, 2011(6): 54-57.
L J Q. Application of X-Ray NDT in Casting Aluminum Alloy Wheel[J]. Special Casting & Nonferrous Alloys, 2013, 33(2): 155-156.
LIU J, TAO W D. Application of X-Ray Digital Radiography in Casting Aluminum Alloy Wheel[J]. Nondestructive Testing Technology, 2017, 41(3): 42-44.
[20]
LI X, TSO S K, GUAN X P, et al. Improving Automatic Detection of Defects in Castings by ApplyingWaveletTechnique[J]. IEEE Transactions on Industrial Electronics, 2006, 53: 1927-1934.
MERY D, CHACON M, MUNOZ L, et al. Automated inspection of aluminium castings using classifier fusion strategies. [J]. Materials Evaluation, 2005, 63(2): 148-153.
[34]
OSMAN A, KAFTAN D JIAN V, HASSLER U. Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory[J]. Pattern Recognition Letters, 2011, 32(2): 168-180.
DING J, WANG M Q, ZHANG J S, et al. Defect Enhancement and Character Removal Technology of Defect Detection of Automobile Hub[J]. Science Technology and Engineering, 2017, 17(35): 76-81.
[41]
丁杰. 汽车轮毂缺陷自动分割与识别技术研究[D]. 太原: 中北大学, 2018.
[43]
MERY D, RIFFO V, ZSCHERPEL U, et al. GDXray: The Database of X-ray Images for Nondestructive Testing[J]. Journal of Nondestructive Evaluation, 2015, 34(4): 1-12.
[45]
FERGUSON M, AK R, LEE Y , et al. Automatic localization of casting defects with convolutional neural networks[C]//IEEE. IEEE International Conference on Big Data (Big Data). Boston, USA, 2017: 1726-1735.
[49]
HAN K, SUN M, ZHOU X, et al. A new Method in Wheel Hub Surface Defect Detection: Object Detection Algorithm Based on Deep Learning[C]//IEEE. International Conference on Advanced Mechatronic Systems (ICAMechS). Xiamen, China, 2017: 335-338.
[50]
韩凯. 基于深度学习的汽车轮毂表面缺陷在线检测算法[D]. 北京: 北京邮电大学, 2019.
[52]
SUN X H, GU J N, HUANG R, et al. Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN[J]. Electronics. 2019, 8(5): 481.
SUN X H, GU J N, WANG M M, et al. Wheel Hub Defects Image Recognition Base on Zero-Shot Learning[J]. Applied Sciences. 2021, (11): 1529.
DONG Z T, LIU B, HU C H, et al. Research on surface defect detection method of screen printing template based on machine vision[J]. High Technology Letters, 2020, 30(12): 1309-1316.
LIU M L, XIE J, YU G G, et al. On-line nondestructive testing of automotive aluminum alloy wheels [J]. Nondestructive Testing Technology, 2000(2): 41-43.
ZHANG S X, CHENG Y Y. Research on defect edge detectionon on auto rims image by using SUSAN operation[J]. Information Technology, 2009, 33(4): 74-76.
ZHAO N N, TAO Y, LI F, et al. Visual saliency superpixel image detection method for surface defect of high-speed rail hub surface defect of high-speed rail hub[J]. Science Technology and Engineering, 2019, 19(32): 230-235.
LI F, TANG Y J, YUAN W Q. The research on surface defects detection of motorcycle hub[J]. Machinery Design & Manufacture, 2020(8): 296-299.
[46]
FERGUSON M, LEE Y T, NARAYANAN A, et al. A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection[J]. Smart and sustainable manufacturing systems, 2019, 3(1): 79-97.
WANG C, ZHANG X F, LIU C, et al. Detection method of wheel hub weld defects based on the improved YOLOv3[J]. Optics and Precision Engineering, 2021, 29(8): 1942-1954.
[54]
Yu Y L, Wang M L, Wang Z M, et al. Surface Defect Detection of Hight-speed Railway Hub Based on Improved YOLOv3 Algorithm[C]//IEEE. 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, 2021: 1386-1390.
GUO R Q, WANG M Q, ZHANG J S, et al. Hub Defect Segmentation Based on U-Net Convolutional Neural Network[J]. Automation & Instrumentation, 2020, 35(4): 43-47.
Cheng S H, Lu J X, Zhang D F, et al. Wheel Hub Identification of Convolutional Neural Networks Based on Ring Features[J]. Acta Metrologica Sinica, 2022, 43(11): 1404-1411.
HE Z X, ZHANG S Y, HUANG C L. Approach to detecting defects in wheels based on flaws’ characteristics and seeded region growing method[J]. Journal of Zhejiang University(Engineering Science), 2009, 43(7): 1230-1237.
ZHANG J S, WANG M Q, GUO Y L, et al. Analysis of X-ray digital imaging system resolution[J]. CT Theory and Applications, 2011, 20(2): 227-234.
DENG Y L. Research on the Method of Defect Detection of Hub Bearings Based on Machine Vision[J]. China Plant Engineering, 2019(13): 106-108.
[32]
HERNNDEZ S, SAEZ D, MERY D. Neuro-Fuzzy Method for Automated Defect Detection in Aluminium Castings[C]// Association for Image and Machine Intelligence. International Conference on Image Analysis and Recognition(ICIAR). Porto, Portugal, 2004: 826-833.
ZHANG G S, ZHANG F, ZOU X, et al. Research on Wheel Hub Defect Detection Based on Improved Genetic Algorithm[J]. Agricultural Equipment & Vehicle Engineering, 2021, 59(2): 100-104.
MERY D, ARTETA C. Automatic Defect Recognition in X-Ray Testing Using Computer Vision[C]//IEEE. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). Santa Rosa, USA, 2017: 1026-1035.
[48]
MERY D. Aluminum Casting Inspection using Deep Object Detection Methods and Simulated Ellipsoidal Defects[J]. Machine Vision and Applications, 2021, 32(3): 1-16.
GUO R Q, WANG M Q, ZHANG J S, et al. Automatic segmentation Technology of Automobile Wheel Hub Defects Based on Deep Learning[J].Science Technology and Engineering, 2020, 20(24): 9976-9981.
SHEN Z Z, DANG H, SUN M Y, et al. Application of Generating Adversarial Networks in High-light Removal of Wheel Hub Surface[C]//2019 International Conference on Advanced Mechatronic Systems (ICAMechS). 2019: 12-15.
SONG H, LI Z. Design of visual inspection system for automobile hub surface defect based on industrial robot[J]. Computer Measurement and Control, 2018, 26(9): 13-16.
JIAO T Y, WANG M Q, ZHANG J S, et al. Hub X-ray image enhancement based on wavelet analysis and pseudo color processing[J]. Automation & Instrumentation, 2020, 35(1): 47-51.
ZHAO H W, ZHAO Y C, QI X Y, et al. Research on surface defect inspection algorithms of automobile hub based on deep learning[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2019(11): 112-115.
WANG T R, WANG M Q, ZHANG J S, et al. Automatic instance segmentation technology of automobile wheel hub defects based on Mask R-CNN[J]. Foreign Electronic Measurement Technology, 2021, 40(2): 1-5.
GUO Z J, WANG M Q, ZHANG J S, et al. The Research on Online Recognition and Classification Technology of Wheel Hub Based on Machine Vision[J]. Journal of Test and Measurement Technology, 2019, 33(3): 233-237.
[31]
MERY D, SILVA R R D, CALOBA L P, et al. Pattern recognition in the automatic inspection of aluminium castings[J]. Insight-Non-Destructive Testing and Condition Monitoring, 2003, 45(7): 475-483.
MERY D. Aluminum casting inspection using deep learning: a method based on convolutional neural networks[J]. Journal of Nondestructive Evaluation, 2020, 39(1): 1-12.
TANG C W, FENG X X, WEN H T, et al. Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature[J]. Applied Sciences. 2021, 11(22): 10508.
YAO B, WEN X L, JIAO L B, et al. Improved YOLOv3 Algorithm for Surface Defect Detection of Aluminum Profile[J]. Acta Metrologica Sinica, 2022, 43(10): 1256-1261.
WU F H, CUI J X, ZHANG N, et al. Surface Defect Detection of Wheel Hub Based on Improved YOLOv4 Algorithm[J]. Acta Metrologica Sinica, 2022, 43(11): 1404-1411.