Table Of ContentTransactions on
Intelligent Welding
Manufacturing
Volume III No. 4 2019
Transactions on Intelligent Welding
Manufacturing
Editors-in-Chief
ShanbenChen YumingZhang ZhiliFeng
ShanghaiJiaoTongUniversity DepartmentofElectrical OakRidgeNationalLaboratory
Shanghai,China andComputerEngineering OakRidge,TN,USA
UniversityofKentucky
Lexington,KY,USA
HonoraryEditors
G.Cook,USA S.J.Na,KOR T.Lienert,USA
K.L.Moore,USA LinWu,PRC T.J.Tarn,USA
Ji-LuanPan,PRC Y.Hirata,JAP
S.A.David,USA J.Norrish,AUS
GuestEditors
H.P.Chen,USA X.Q.Chen,NZL D.Du,PRC D.Fan,PRC
J.C.Feng,PRC D.Hong,USA X.D.Jiao,PRC I.Lopez-Juarez,MEX
H.J.Li,AUS W.Zhou,SGP
RegionalEditors
Asia:L.X.Zhang,PRC Australia:Z.X.Pan,AUS
America:Y.K.Liu,USA Europe:S.Konovalov,RUS
AssociateEditors
Q.X.Cao,PRC Y.Huang,USA PedroNeto,PRT S.Wang,PRC
B.H.Chang,PRC S.Konovalov,RUS G.Panoutsos,UK X.W.Wang,PRC
J.Chen,USA W.H.Li,PRC Z.X.Pan,AUS Z.Z.Wang,PRC
H.B.Chen,PRC X.R.Li,USA X.D.Peng,NL G.J.Zhang,PRC
S.J.Chen,PRC Y.K.Liu,USA Y.Shi,PRC H.Zhang,B,PRC
X.Z.Chen,PRC L.M.Liu,PRC J.Wu,USA H.Zhang,N,PRC
A.-K.Christiansson,SWE H.Lu,PRC J.X.Xue,PRC L.X.Zhang,PRC
Z.G.Li,PRC Z.Luo,PRC L.J.Yang,PRC W.J.Zhang,USA
X.M.Hua,PRC G.H.Ma,PRC M.Wang,PRC
AcademicAssistantEditors
J.Cao,PRC S.B.Lin,PRC S.L.Wang,PRC H.W.Yu,PRC
B.Chen,PRC Y.Shao,USA J.Xiao,PRC K.Zhang,PRC
Y.Luo,PRC Y.Tao,PRC J.J.Xu,PRC W.Z.Zhang,PRC
N.Lv,PRC J.J.Wang,PRC Y.L.Xu,PRC Z.F.Zhang,PRC
F.Li,PRC H.Y.Wang,PRC C.Yu,PRC
EditorialStaff
ExecutiveEditor(ManuscriptandPublication): Dr.YanZhang,PRC
ResponsibleEditors(AcademicandTechnical): Dr.NaLv,PRC
Dr.JingWu,USA
More information about this series at http://www.springer.com/series/15698
Shanben Chen Yuming Zhang Zhili Feng
(cid:129) (cid:129)
Editors
Transactions on Intelligent
Welding Manufacturing
Volume III No. 4 2019
123
Editors
Shanben Chen YumingZhang
ShanghaiJiao Tong University Department ofElectrical andComputer
Shanghai, China Engineering
University of Kentucky
Zhili Feng Lexington, KY,USA
Oak RidgeNational Laboratory
Oak Ridge,TN, USA
ISSN 2520-8519 ISSN 2520-8527 (electronic)
Transactions onIntelligent Welding Manufacturing
ISBN978-981-33-6501-8 ISBN978-981-33-6502-5 (eBook)
https://doi.org/10.1007/978-981-33-6502-5
©SpringerNatureSingaporePteLtd.2021
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Editorials
This issue of the Transactions on Intelligent Welding Manufacturing (TIWM) is
also a collection in part selected from the high-quality contributions recommended
by “The 2019 International Workshop on Intelligentized Welding Manufacturing
(IWIWM’2019).” It includes two feature articles, eight research papers and two
short papers.
The first featured article in this issue, “Multi-layer Multi-pass Welding of
Medium Thickness Plate: Technologies, Advances and Future Prospects”, is con-
tributedbyFengjingXu,ShanbenChenandYanlingXufromShanghaiJiaoTong
University. This paper discusses the research status of multi-layer multi-pass
(MLMP) welding of medium thickness plates and welding simulation process in
detail. Novel feature extraction methods based on different sensing techniques are
also summarized in this paper.
The second featured article in this issue, “A Review: Application Research of
Intelligent 3D Detection Technology Based on Linear-Structured Light”, is con-
tributedbyShaojieChen,WeiTao,HuiZhaoandNaLv,fromShanghaiJiaoTong
University.Thispaperanalyzesthelatestapplicationandresearchofline-structured
light sensingtechnology intheindustrialapplicationofworkpieceobjectdetection
and positioning, geometric profile measurement, three-dimensional reconstruction.
The first research article, “Acoustic Emission-Based Weld Crack In-situ
Detection and Location Using WT-TDOA”, is contributed by Zhifen Zhang, Rui
QinandGuangruiWenallfromXi’anJiaoTongUniversity.Thispaperproposesa
time difference of arrival (TDOA) method based on wavelet transform (WT) to
enabletheaccurate locationofweldcracks atthebeginningofitsappearance.The
results of the study show that TDOA based on wavelet transform (WT-TDOA)
outperformed the conventional TDOA significantly in terms of location accuracy.
The second research paper is entitled “The Research of Real-Time Welding
QualityDetectionviaVisualSensorforMIGWeldingProcess”.Itisacontribution
from a research team at Beibu Gulf University. A real-time monitoring system
basedonvisualsensingtechnologyisproposed,aimingattheonlinemonitoringof
weld quality in welding process. Based on the ROI visual attention mechanism,
images of the front and back of the pool were extracted. A random forest special
v
vi Editorials
fusion model based on weld parameters and image features is constructed, which
realizes the recognition of weld penetration status classification and the regression
prediction of weld back penetration width.
The third research paper titled “A Weld Bead Profile Extraction Method Based
onScanningMonocularStereoVisionforMulti-layerMulti-passWeldingonMid-
thickPlate”iscontributedbyaresearchteamfromShanghaiJiaoTongUniversity.
This paper studies multi-layer multi-pass welding (MLMPW). In this paper,
scanning monocular stereo vision for MLMPW is used to reconstruct the weld
bead. Through the slicing and filtering of the point cloud data, the profile of the
weld bead surface is obtained, which provides a solid foundation for MLMPP and
its online correction.
The fourth research paper, “The Intelligent Methodology for Monitoring the
Dynamic Welding Quality Using Visual and Audio Sensor”, is co-authored by
Zhiqiang Feng, Ziquan Jiao and Junfeng Han affiliated with the Beibu Gulf
University, China. In this paper, a real-time welding quality prediction scheme
basedonmulti-informationfusionisproposed.Theresultsshowthatthearcsound
andvisualinformationcouldcomplementeachotherandbeeffectivelycombinedto
achieve adequate online welding quality monitoring.
The fifth research paper, “Convolutional Neural Network Prediction of
Aluminum Alloy GTAW Penetration Process Based on Arc Sound Sensing”, is
contributed by a research team from Shanghai Jiao Tong University. This paper
uses industrial Internet of things (IoT) to design a set of technical solutions,
uploading various data collected during the welding process to the cloud for stor-
age, and to remotely monitor the welding process in real time through a browser.
A weld penetration state classification model based on convolutional neural net-
works is also established.
The sixth research paper, “Identification and Penetration Prediction of
Aluminum Alloy GTAW Pool Based on Network Vision Monitoring”, is a con-
tributionfromShanghaiJiaoTongUniversity.Weldpenetrationdetectionbasedon
weld pool image intungsteninert gas(TIG)welding has been a hotresearch topic
inindustryandacademia.Inthispaper,apredictionmodelofthemeltwidthonthe
back of the aluminum alloy TIG weld pool and a classification model of the alu-
minum alloy TIG welding state are constructed and XGBoost-based models are
used for real-time prediction of backside bead width.
Theseventhresearch paper inthecollectionis“Research onWeldingTransient
Deformation Monitoring Technology Based on Non-contact Sensor Technology”
contributed from researchers at Beibu Gulf University. This paper tries to analyze
theresearchondetectionofweldingdeformationindifferentsensorytechnologies.
The results show that they could get good result in a different application envi-
ronment for different sensing technologies. The non-contact detection could get
more intelligent and more accurate, and the contact detection is more reliable and
widely used in industry.
The last research paper, “Binocular Stereo Vision and Modified DBSCAN on
Point Clouds for Single Leaf Segmentation”, is a contribution from Shanghai Jiao
Tong University. In this paper, a modified density-based spatial clustering of
Editorials vii
applications with noise (DBSCAN) algorithm based on the above new-defined
distance metric is used to cluster the refined point clouds.
In the category of short papers, “Teaching-Free Intelligent Robotic Welding of
HeterocyclicMediumandThickPlatesBasedonVision”isfromZhejiangNormal
University. It provides technical support for the “intelligent manufacturing”
upgrade of China’s high-end marine engineering equipment and has important
valueinengineeringpromotion.“In-ProcessVisualMonitoringofPenetrationState
in Nuclear Steel Pipe Welding” from Shanghai Jiao Tong University develops a
pipe inner inspection robot equipped with CMOS sensor and laser scanner. The
result shows that this monitoring system has an important impact on the quality
control of the all-position pipe welding process.
This issue of TIWM shows the new perspectives and developments in the field
of intelligent welding research, as well as the topics related to the IWIWM’2019
Conference. The publication of this issue will certainly give readers new inspira-
tion, as we always hope so.
Prof. Yuming Zhang
TIWM Editor-in-Chief
University of Kentucky
Lexington, KY, USA
[email protected]
Contents
Feature Articles
Multi-layer Multi-pass Welding of Medium Thickness Plate:
Technologies, Advances and Future Prospects . . . . . . . . . . . . . . . . . . . . 3
Fengjing Xu, Runquan Xiao, Zhen Hou, Yanling Xu, Huajun Zhang,
and Shanben Chen
A Review: Application Research of Intelligent 3D Detection
Technology Based on Linear-Structured Light. . . . . . . . . . . . . . . . . . . . 35
Shaojie Chen, Wei Tao, Hui Zhao, and Na Lv
Research Papers
Acoustic Emission-Based Weld Crack In-situ Detection and Location
Using WT-TDOA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Zhifen Zhang, Rui Qin, Yujiao Yuan, Wenjing Ren, Zhe Yang,
and Guangrui Wen
The Research of Real-Time Welding Quality Detection via Visual
Sensor for MIG Welding Process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Junfeng Han, Zhiqiang Feng, Ziquan Jiao, and Xiangxi Han
A Weld Bead Profile Extraction Method Based on Scanning
Monocular Stereo Vision for Multi-layer Multi-pass Welding
on Mid-thick Plate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Zhen Hou, Yanling Xu, Runquan Xiao, and Shanben Chen
The Intelligent Methodology for Monitoring the Dynamic Welding
Quality Using Visual and Audio Sensor . . . . . . . . . . . . . . . . . . . . . . . . . 99
Zhiqiang Feng, Ziquan Jiao, Junfeng Han, and Weiming Huang
ConvolutionalNeuralNetworkPredictionofAluminumAlloyGTAW
Penetration Process Based on Arc Sound Sensing . . . . . . . . . . . . . . . . . 115
Zisheng Jiang, Chao Chen, Shanben Chen, and Na Lv
ix
x Contents
Identification and Penetration Prediction of Aluminum Alloy GTAW
Pool Based on Network Vision Monitoring . . . . . . . . . . . . . . . . . . . . . . 131
YiLei Luo, Chao Chen, ZiSheng Jiang, and Shanben Chen
Research on Welding Transient Deformation Monitoring Technology
Based on Non-contact Sensor Technology . . . . . . . . . . . . . . . . . . . . . . . 149
Ziquan Jiao, Zhiqiang Feng, Junfeng Han, and Weiming Huang
Binocular Stereo Vision and Modified DBSCAN on Point Clouds
for Single Leaf Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
Chengyu Tao, Na Lv, and Shanben Chen
Short Papers and Technical Notes
Teaching-Free Intelligent Robotic Welding of Heterocyclic Medium
and Thick Plates Based on Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Hu Lan, Huajun Zhang, Jun Fu, Libin Gao, and Liang Wei
In-Process Visual Monitoring of Penetration State in Nuclear
Steel Pipe Welding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
Liangrui Wang, Shu’ang Wang, Weihua Liu, Yuefeng Chen,
and Huabin Chen
Information for Authors. .... ..... .... .... .... .... .... ..... .... 201
Author Index.. .... .... .... ..... .... .... .... .... .... ..... .... 203
