On-line quality inspection in laser blank welding using ART2 neural network

被引：0

作者：

邹媛媛 ^{[1
]}

赵明扬 ^{[2
]}

张雷 ^{[1
]}

机构：

[1] Shenyang Institute of Automation Chinese Academy of Sciences,Shenyang,110016. Graduate School of the Chinese Academy of Sciences,Beijing,100039.

[2] Shenyang Institute of Automation Chinese Academy of Sciences,Shenyang,110016.

来源：

China Welding | 2006年 / 04期

关键词：

quality inspection; laser blank welding; neural network; ART2;

D O I：

暂无

中图分类号：

TG456.7 [激光焊];

学科分类号：

080201 ; 080503 ;

摘要：

Laser blank welding is becoming more and more important in the automotive industry and the quality of the weld is critical for a successful application.A fully automated solution is required to inspect the quality of the blanks.This paper presents a vision inspection system with a CMOS camera which uses ART2 network to inspect the defects on-line to obtain the geometry and the quality of the weld seam.The neural network ART2 has the capability of self-learning from the environment. It can distinguish the defects that have been learned before and give new outputs for new defects.So ART2 network is suitable for weld quality inspection in laser blank welding.Additionally,a CO2laser is used for the blank butt-welding.

引用

页码：51 / 54

页数：4

共 50 条

[31] DESCRIBING AND SIMULATION OF THE GENERALIZED NET MODEL OF THE ART2 NEURAL NETWORK
Petkov, Todor
Sotirov, Sotir
COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES, 2015, 68 (11): : 1431 - 1438
[32] Part family formation through fuzzy ART2 neural network
Kuo, R. J.
Su, Y. T.
Chiu, C. Y.
Chen, Kai-Ying
Tien, F. C.
DECISION SUPPORT SYSTEMS, 2006, 42 (01) : 89 - 103
[33] On-Line Welding Quality Inspection System for Steel Pipe Based on Machine Vision
Yang, Yang
MATERIALS SCIENCE, ENERGY TECHNOLOGY, AND POWER ENGINEERING I, 2017, 1839
[34] Model-based fault detection and isolation method using ART2 neural network
Lee, IS
Kim, JT
Lee, JW
Lee, DY
Kim, KY
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2003, 18 (10) : 1087 - 1100
[35] A strategy for acquiring customer requirement patterns using laddering technique and ART2 neural network
Chen, CH
Khoo, LP
Yan, W
ADVANCED ENGINEERING INFORMATICS, 2002, 16 (03) : 229 - 240
[36] An adaptive intrusion detection model based on ART2 neural network
Liu, J. (jifenliu@163.com), 1600, Binary Information Press, P.O. Box 162, Bethel, CT 06801-0162, United States (09):
[37] Customer requirements spurring innovation based on ART2 neural network
Luo, Jianhong
Pan, Xuwei
ADVANCING SCIENCE THROUGH COMPUTATION, 2008, : 381 - 383
[38] AN ORDER CLUSTERING SYSTEM USING ART2 NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION METHODN
Kuo, R. J.
Wang, M. J.
Huang, T. W.
Hu, Tung-Lai
ICEIS 2009 : PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL AIDSS, 2009, : 54 - 59
[39] On-line inspection and accuracy analysis for parts using neural networks
Xiong, YG
Zhang, GZ
MACHINE VISION SYSTEMS FOR INSPECTION AND METROLOGY VII, 1998, 3521 : 168 - 178
[40] A new learning algorithm of ARTMAP using the ART2 network
Hori, Yoshinari
Yamada, Akihiro
KAGAKU KOGAKU RONBUNSHU, 2007, 33 (01) : 38 - 42

← 1 2 3 4 5 →