Artificial neural network modelling of cold-crack resistance of high strength low alloy steel 950A

The objective of the study is to predict the cold cracking resistance of high strength low alloy 950A welded joints using an artificial neural network (ANN) model. A bead on plate welding is carried out using the gas metal arc welding process. The identified process parameters for the ANN are prehea...

Full description

Saved in:
Bibliographic Details
Published inJournal of engineering (Stevenage, England) Vol. 2019; no. 2; pp. 447 - 454
Main Authors Manivelmuralidaran, Velumani, Senthilkumar, Krishnasamy
Format Journal Article
LanguageEnglish
Published The Institution of Engineering and Technology 01.02.2019
Wiley
Subjects
alloy steel
ANN
arc welding
artificial neural network model
artificial neural network modelling
cold cracking resistance
cold-crack resistance
cracks
current 950.0 A
experimental data
gas metal arc welding process
given input parameters
heat input
high strength low
identified process parameters
impact strength
neural nets
oxide particle content
oxide particles content
plate welding
plates (structures)
predicted observed values
preheating temperature
propagation model
Research Article
sensitivity analysis
weld metal
welded joints
welding
welds
impact strength
ANN
sensitivity analysis
plate welding
gas metal arc welding process
heat input
predicted observed values
welds
welded joints
identified process parameters
artificial neural network model
cracks
current 950.0 A
preheating temperature
oxide particle content
oxide particles content
welding
cold-crack resistance
high strength low
alloy steel
plates (structures)
weld metal
arc welding
cold cracking resistance
propagation model
given input parameters
artificial neural network modelling
neural nets
experimental data
Online AccessGet full text

Cover

Abstract The objective of the study is to predict the cold cracking resistance of high strength low alloy 950A welded joints using an artificial neural network (ANN) model. A bead on plate welding is carried out using the gas metal arc welding process. The identified process parameters for the ANN are preheating temperature, oxide particle content, and heat input. The impact strength of the weld metal is considered as the output parameter. A feed-forward back propagation model with ten neurons in the hidden layer is developed to predict the impact strength of the weld metal. The neural network model is created, trained, and tested with a set of experimental data. The proposed model correctly predicted the impact strength of the given input parameters. The predicted value of the impact strength is in agreement with the experimental data. The error percentage between the predicted and observed values is <5% and the root mean square error value is 2.2%. Sensitivity analysis is performed to identify the significance of input parameters. It is evident that the preheating temperature contributes 50.04%, oxide particles content contributes 37.15%, and heat input contributes 12.81% to impact strength.
AbstractList The objective of the study is to predict the cold cracking resistance of high strength low alloy 950A welded joints using an artificial neural network (ANN) model. A bead on plate welding is carried out using the gas metal arc welding process. The identified process parameters for the ANN are preheating temperature, oxide particle content, and heat input. The impact strength of the weld metal is considered as the output parameter. A feed-forward back propagation model with ten neurons in the hidden layer is developed to predict the impact strength of the weld metal. The neural network model is created, trained, and tested with a set of experimental data. The proposed model correctly predicted the impact strength of the given input parameters. The predicted value of the impact strength is in agreement with the experimental data. The error percentage between the predicted and observed values is <5% and the root mean square error value is 2.2%. Sensitivity analysis is performed to identify the significance of input parameters. It is evident that the preheating temperature contributes 50.04%, oxide particles content contributes 37.15%, and heat input contributes 12.81% to impact strength.
Author Manivelmuralidaran, Velumani
Senthilkumar, Krishnasamy
Author_xml – sequence: 1
  givenname: Velumani
  surname: Manivelmuralidaran
  fullname: Manivelmuralidaran, Velumani
  email: manivelmuralidaran.v.mec@kct.ac.in
  organization: 1Department of Mechanical Engineering, Kumaraguru College of Technology, Coimbatore, India
– sequence: 2
  givenname: Krishnasamy
  surname: Senthilkumar
  fullname: Senthilkumar, Krishnasamy
  organization: 2Department of Mechanical Engineering, Adithya Institute of Technology, Coimbatore, India
BookMark eNp9kL1v3DAMxYUiAZqmGbtr7eALZUm2PF6DfCJAl3QW9EHd6aJYhezgcP995VxQdEknEuTje-DvCzkZ84iEfGOwYiCGy13GVQtMrWTb95_IWQuSNZyDPPmn_0wupmkHAIyLFgQ7I3Zd5hiiiybREV_LW5n3uTzTl-wxpThuaA7U5eQbV4x7pgWnOM1mdLgstnGzpdNccNzMW5rynpqU8qGOEBMdJKy_ktNg0oQX7_Wc_Lq5frq6ax5_3t5frR8bx2HoGy6CZwEVc9CpYBl2jEulmOWdgADghx68cZYLJZ2oP0vheNcr691gJZf8nNwffX02O_27xBdTDjqbqN8GuWy0qc-6hBqFVei5rRwGYX0N7VmregjeGmgZq17N0cuVPE0Fw18_BnrhrStvvfDWC--q7476fUx4-L9YPz1ctz9ugEG3HH4_Hkacq-y1jBXRByF_AFfskaw
Cites_doi 10.2355/isijinternational.44.1201
10.3365/KJMM.2015.53.11.778
10.1016/j.dt.2016.09.003
10.1016/j.measurement.2016.09.041
10.1155/2013/574914
10.1016/j.matdes.2010.12.007
10.1007/s10845-011-0526-4
10.1088/1757-899X/103/1/012034
10.1016/j.msea.2011.01.031
10.1016/j.msea.2018.02.021
10.1016/j.asoc.2009.10.007
10.1088/1757-899X/115/1/012002
10.1016/j.acme.2013.10.010
10.1016/j.msea.2011.07.035
10.1557/jmr.2016.176
10.1016/j.msea.2012.04.076
10.1007/s11630-010-0411-z
10.1016/j.matdes.2014.02.057
10.1080/09500839.2014.976286
10.1179/136217104225012265
ContentType Journal Article
Copyright 2021 The Institution of Engineering and Technology
Copyright_xml – notice: 2021 The Institution of Engineering and Technology
DBID IDLOA
24P
WIN
AAYXX
CITATION
DOA
DOI 10.1049/joe.2018.5277
DatabaseName IET Digital Library (Open Access)
Wiley Online Library Open Access
Wiley Online Library Journals
CrossRef
Directory of Open Access Journals
DatabaseTitle CrossRef
DatabaseTitleList


Database_xml – sequence: 1
  dbid: IDLOA
  name: IET Digital Library (Open Access)
  url: https://digital-library.theiet.org/content/collections
  sourceTypes: Publisher
– sequence: 2
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 3
  dbid: 24P
  name: Wiley Online Library Open Access
  url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 2051-3305
EndPage 454
ExternalDocumentID oai_doaj_org_article_e4b8ed3b01394bde81712870fdba0211
10_1049_joe_2018_5277
TJE2BF01067
Genre article
GroupedDBID 24P
5VS
AAJGR
ADACO
ADBBV
ALMA_UNASSIGNED_HOLDINGS
BCNDV
BFFAM
GROUPED_DOAJ
IDLOA
KQ8
OCL
OK1
RIE
RIG
RNS
RUI
0R~
1OC
AAHJG
ABJCF
ABQXS
ACESK
ACXQS
AFKRA
ARAPS
AVUZU
BENPR
BGLVJ
CCPQU
EBS
HCIFZ
IAO
IGS
IPNFZ
M43
M7S
M~E
PIMPY
PTHSS
ROL
WIN
AAYXX
CITATION
ITC
ID FETCH-LOGICAL-c3097-34fd1fe81c068fb1e6135881b3640f00d970dacb3485c410454c3678bdc9b5353
IEDL.DBID IDLOA
ISSN 2051-3305
IngestDate Tue Oct 22 15:13:15 EDT 2024
Thu Sep 26 17:19:21 EDT 2024
Sat Aug 24 01:05:17 EDT 2024
Tue Jan 05 21:45:38 EST 2021
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Keywords impact strength
ANN
sensitivity analysis
plate welding
gas metal arc welding process
heat input
predicted observed values
welds
welded joints
identified process parameters
artificial neural network model
cracks
current 950.0 A
preheating temperature
oxide particle content
oxide particles content
welding
cold-crack resistance
high strength low
alloy steel
plates (structures)
weld metal
arc welding
cold cracking resistance
propagation model
given input parameters
artificial neural network modelling
neural nets
experimental data
Language English
License Attribution
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c3097-34fd1fe81c068fb1e6135881b3640f00d970dacb3485c410454c3678bdc9b5353
OpenAccessLink http://digital-library.theiet.org/content/journals/10.1049/joe.2018.5277
PageCount 8
ParticipantIDs wiley_primary_10_1049_joe_2018_5277_TJE2BF01067
doaj_primary_oai_doaj_org_article_e4b8ed3b01394bde81712870fdba0211
crossref_primary_10_1049_joe_2018_5277
iet_journals_10_1049_joe_2018_5277
ProviderPackageCode IDLOA
RUI
PublicationCentury 2000
PublicationDate February 2019
PublicationDateYYYYMMDD 2019-02-01
PublicationDate_xml – month: 02
  year: 2019
  text: February 2019
PublicationDecade 2010
PublicationTitle Journal of engineering (Stevenage, England)
PublicationYear 2019
Publisher The Institution of Engineering and Technology
Wiley
Publisher_xml – name: The Institution of Engineering and Technology
– name: Wiley
References Cai, Y.C.; Liu, R.P.; Wei, Y.H. (C6) 2014; 62
Fattahi, M.N.; Nabhani, M.R.; Vaezi Rahimi, E. (C3) 2011; 528
Ghosh, P.K.; Singh, U. (C13) 2004; 9
Lina, L.; Li, B.-S.; Zhu, G.-M. (C4) 2018; 721
Chandra, R.K.; Majid, M.; Arya, H.K. (C18) 2016; 4
Louly, M.A.; Lemine, O.M.; Gharbi, A. (C21) 2017; 95
Sarkar, A.; Dey, P.; Rai, R.N. (C22) 2016; 41
Senthilkumar, K.; Srinivasan, P.S.S. (C23) 2010; 19
Chokkalingham, S.; Chandrasekhar, N.; Vasudevan, M. (C20) 2012; 23
Hejazi, D.; Haq, A.J.; Yazdipour, N. (C5) 2012; 551
Maleki, E. (C15) 2015; 103
Shojaeefard, M.H.; Akbari, M.; Tahani, M. (C26) 2013; 2013
Lan, L.; Kong, X.; Hu, Z. (C28) 2014; 94
Sudhakar, U.; Srinivas, J. (C14) 2016; 115
Kumar, A.; Chauhan, V.; Bist, A.S. (C17) 2013; 67
Vimalraj, C.; Kah, P.; Mvola, B. (C10) 2016; 44
Chakraborty, G.; Rejeesh, R.; Albert, S.K. (C8) 2016; 12
Węglowski, M.St.; Zeman, M. (C12) 2013; 14
Ghosh, P.K.; Gupta, P.C.; Potluri, N.B. (C11) 2004; 44
Nagesh, D.; Datta, G. (C24) 2010; 10
Hu, L.H.; Huang, J.; Li, Z.G. (C27) 2011; 32
Sung, H.K.; Shin, S.Y.; Cha, W. (C2) 2011; 528
Senthilkumar, B.; Kannan, T. (C25) 2014
Bang, K.-S.; Jo, Y.-J.; Han, I.-W. (C7) 2015; 53
Xing, X.; Zhou, Y.; Wang, J. (C9) 2016; 31
Shamith, L.S.; Kalaichelvi, V.; Karthikeyan, R. (C19) 2018; 346
2004; 44
2010; 10
2018; 721
2010; 19
2015; 103
2013; 67
2015; 53
2018; 346
2004; 9
2009
2016; 31
2011; 32
2004
2014; 62
2016; 12
2016; 4
2017; 95
2012; 551
2013; 14
2013; 2013
2011; 528
2016; 41
2016; 115
2014
2012; 23
2014; 94
2016; 44
Chandra R.K. (e_1_2_8_19_1) 2016; 4
Chakraborty G. (e_1_2_8_9_1) 2016; 12
Sudhakar U. (e_1_2_8_15_1) 2016; 115
Sung H.K. (e_1_2_8_3_1) 2011; 528
Bang K.‐S. (e_1_2_8_8_1) 2015; 53
Kumar A. (e_1_2_8_18_1) 2013; 67
Lan L. (e_1_2_8_29_1) 2014; 94
Cai Y.C. (e_1_2_8_7_1) 2014; 62
e_1_2_8_2_1
Hu L.H. (e_1_2_8_28_1) 2011; 32
Ghosh P.K. (e_1_2_8_14_1) 2004; 9
Shamith L.S. (e_1_2_8_20_1) 2018; 346
Louly M.A. (e_1_2_8_22_1) 2017; 95
Senthilkumar B. (e_1_2_8_26_1) 2014
Hejazi D. (e_1_2_8_6_1) 2012; 551
Ghosh P.K. (e_1_2_8_12_1) 2004; 44
Węglowski M.St. (e_1_2_8_13_1) 2013; 14
e_1_2_8_17_1
Shojaeefard M.H. (e_1_2_8_27_1) 2013; 2013
Vimalraj C. (e_1_2_8_11_1) 2016; 44
Maleki E. (e_1_2_8_16_1) 2015; 103
Xing X. (e_1_2_8_10_1) 2016; 31
Chokkalingham S. (e_1_2_8_21_1) 2012; 23
Nagesh D. (e_1_2_8_25_1) 2010; 10
Senthilkumar K. (e_1_2_8_24_1) 2010; 19
Lina L. (e_1_2_8_5_1) 2018; 721
Fattahi M.N. (e_1_2_8_4_1) 2011; 528
e_1_2_8_30_1
Sarkar A. (e_1_2_8_23_1) 2016; 41
References_xml – volume: 551
  start-page: 40
  year: 2012
  end-page: 49
  ident: C5
  article-title: Effect of manganese content and microstructure on the susceptibility of X70 pipeline steel to hydrogen cracking
  publication-title: Mater. Sci. Eng. A
  contributor:
    fullname: Hejazi, D.; Haq, A.J.; Yazdipour, N.
– volume: 14
  start-page: 417
  year: 2013
  end-page: 424
  ident: C12
  article-title: Prevention of cold cracking in ultra-high-strength steel Weldox 1300
  publication-title: Arch. Civ. Mech. Eng.
  contributor:
    fullname: Węglowski, M.St.; Zeman, M.
– volume: 31
  start-page: 1702
  issue: 12
  year: 2016
  end-page: 1710
  ident: C9
  article-title: Effects of rare earth oxide Y O on microstructure and mechanical properties of pro-eutectoid ferrite/granular bainitic coating
  publication-title: Int. J. Mater. Res.
  contributor:
    fullname: Xing, X.; Zhou, Y.; Wang, J.
– year: 2014
  ident: C25
  article-title: Effect of flux cored arc welding process parameters on bead geometry in super duplex stainless steel claddings
  publication-title: Measurement
  contributor:
    fullname: Senthilkumar, B.; Kannan, T.
– volume: 53
  start-page: 778
  issue: 11
  year: 2015
  end-page: 784
  ident: C7
  article-title: Improvement of weld metal fracture toughness by micro-addition of rare earth metal
  publication-title: Korean J. Met. Mater.
  contributor:
    fullname: Bang, K.-S.; Jo, Y.-J.; Han, I.-W.
– volume: 346
  start-page: 1
  year: 2018
  end-page: 8
  ident: C19
  article-title: Prediction analysis of weld-bead and heat affected zone in TIG welding using artificial neural networks
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
  contributor:
    fullname: Shamith, L.S.; Kalaichelvi, V.; Karthikeyan, R.
– volume: 721
  start-page: 38
  year: 2018
  end-page: 46
  ident: C4
  article-title: Effect of niobium precipitation behavior on microstructure and hydrogen induced cracking of press hardening steel 22MnB5
  publication-title: Mater. Sci. Eng. A
  contributor:
    fullname: Lina, L.; Li, B.-S.; Zhu, G.-M.
– volume: 103
  start-page: 1
  year: 2015
  end-page: 15
  ident: C15
  article-title: Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
  contributor:
    fullname: Maleki, E.
– volume: 115
  start-page: 1
  year: 2016
  end-page: 8
  ident: C14
  article-title: Impact resistance and hardness modelling of aluminium alloy welds using square-headed friction-stir welding tool
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
  contributor:
    fullname: Sudhakar, U.; Srinivas, J.
– volume: 95
  start-page: 70
  year: 2017
  end-page: 76
  ident: C21
  article-title: Modeling of the microstructural properties of ( )ZnO(1 −  )Fe O nanocrystalline by artificial neural network and response surface methodology
  publication-title: Measurement
  contributor:
    fullname: Louly, M.A.; Lemine, O.M.; Gharbi, A.
– volume: 67
  start-page: 32
  issue: 1
  year: 2013
  end-page: 37
  ident: C17
  article-title: Role of artificial neural network in welding technology: a survey
  publication-title: Int. J. Comput. Appl.
  contributor:
    fullname: Kumar, A.; Chauhan, V.; Bist, A.S.
– volume: 10
  start-page: 897
  issue: 3
  year: 2010
  end-page: 907
  ident: C24
  article-title: Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process
  publication-title: Appl. Soft Comput.
  contributor:
    fullname: Nagesh, D.; Datta, G.
– volume: 41
  start-page: 549
  issue: 5
  year: 2016
  end-page: 559
  ident: C22
  article-title: A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged-arc welding
  publication-title: Indian Acad. Sci.
  contributor:
    fullname: Sarkar, A.; Dey, P.; Rai, R.N.
– volume: 19
  start-page: 473
  issue: 5
  year: 2010
  end-page: 479
  ident: C23
  article-title: Application of Taguchi method for the optimisation of system parameters of centrifugal evaporative air cooler
  publication-title: J. Therm. Sci.
  contributor:
    fullname: Senthilkumar, K.; Srinivasan, P.S.S.
– volume: 2013
  start-page: 1
  year: 2013
  end-page: 8
  ident: C26
  article-title: Sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass
  publication-title: Adv. Mater. Sci. Eng.
  contributor:
    fullname: Shojaeefard, M.H.; Akbari, M.; Tahani, M.
– volume: 44
  start-page: 1201
  issue: 7
  year: 2004
  end-page: 1210
  ident: C11
  article-title: Influence of pre and post weld heating on weldability of modified 9Cr–1MoVNb steel plates under SMA and GTA welding processes
  publication-title: Trans. Iron Steel Inst. Jpn.
  contributor:
    fullname: Ghosh, P.K.; Gupta, P.C.; Potluri, N.B.
– volume: 12
  start-page: 490
  issue: 6
  year: 2016
  end-page: 495
  ident: C8
  article-title: Study on hydrogen assisted cracking susceptibility of HSLA steel by implant test
  publication-title: Def. Technol.
  contributor:
    fullname: Chakraborty, G.; Rejeesh, R.; Albert, S.K.
– volume: 32
  start-page: 1931
  year: 2011
  end-page: 1939
  ident: C27
  article-title: Effects of preheating temperature on cold cracks, microstructures and properties of high power laser hybrid welded 10Ni3CrMoV steel
  publication-title: Mater. Des.
  contributor:
    fullname: Hu, L.H.; Huang, J.; Li, Z.G.
– volume: 4
  start-page: 94
  issue: 5
  year: 2016
  end-page: 99
  ident: C18
  article-title: Enhancement of impact strength of SAW welded low carbon steels by addition of titanium and manganese in agglomerated flux
  publication-title: Int. J. Mech. Prod. Eng.
  contributor:
    fullname: Chandra, R.K.; Majid, M.; Arya, H.K.
– volume: 528
  start-page: 3350
  year: 2011
  end-page: 3357
  ident: C2
  article-title: Effects of acicular ferrite on charpy impact properties in heat affected zones of oxide-containing API X80 line pipe steels
  publication-title: Mater. Sci. Eng. A
  contributor:
    fullname: Sung, H.K.; Shin, S.Y.; Cha, W.
– volume: 528
  start-page: 8031
  issue: 27
  year: 2011
  end-page: 8039
  ident: C3
  article-title: Improvement of impact toughness of AWS E6010 weld metal by adding TiO nanoparticles to the electrode coating
  publication-title: Mater. Sci. Eng. A
  contributor:
    fullname: Fattahi, M.N.; Nabhani, M.R.; Vaezi Rahimi, E.
– volume: 9
  start-page: 229
  issue: 3
  year: 2004
  end-page: 236
  ident: C13
  article-title: Influence of pre- and post-weld heating on weldability of modified 9Cr–1Mo(V–Nb) steel pipe under shielded metal arc and tungsten inert gas welding processes
  publication-title: Sci. Technol. Weld. Joining
  contributor:
    fullname: Ghosh, P.K.; Singh, U.
– volume: 94
  start-page: 764
  issue: 12
  year: 2014
  end-page: 771
  ident: C28
  article-title: Hydrogen- induced cold cracking in heat-affected zone of low-carbon high-strength steel
  publication-title: Philos. Mag. Lett.
  contributor:
    fullname: Lan, L.; Kong, X.; Hu, Z.
– volume: 23
  start-page: 1995
  issue: 5
  year: 2012
  end-page: 2001
  ident: C20
  article-title: Predicting the depth of penetration and weld bead width from the infrared thermal image of the weld pool using artificial neural network modeling
  publication-title: J. Intell. Manuf.
  contributor:
    fullname: Chokkalingham, S.; Chandrasekhar, N.; Vasudevan, M.
– volume: 62
  start-page: 83
  year: 2014
  end-page: 90
  ident: C6
  article-title: Influence of Y on microstructures and mechanical properties of high strength steel weld metal
  publication-title: Mater. Des.
  contributor:
    fullname: Cai, Y.C.; Liu, R.P.; Wei, Y.H.
– volume: 44
  start-page: 370
  year: 2016
  end-page: 382
  ident: C10
  article-title: Effect of nanomaterial addition using GMAW and GTAW processes
  publication-title: Rev. Adv. Mater. Sci.
  contributor:
    fullname: Vimalraj, C.; Kah, P.; Mvola, B.
– volume: 14
  start-page: 417
  year: 2013
  end-page: 424
  article-title: Prevention of cold cracking in ultra‐high‐strength steel Weldox 1300
  publication-title: Arch. Civ. Mech. Eng.
– year: 2009
– volume: 67
  start-page: 32
  issue: 1
  year: 2013
  end-page: 37
  article-title: Role of artificial neural network in welding technology: a survey
  publication-title: Int. J. Comput. Appl.
– volume: 551
  start-page: 40
  year: 2012
  end-page: 49
  article-title: Effect of manganese content and microstructure on the susceptibility of X70 pipeline steel to hydrogen cracking
  publication-title: Mater. Sci. Eng. A
– volume: 44
  start-page: 1201
  issue: 7
  year: 2004
  end-page: 1210
  article-title: Influence of pre and post weld heating on weldability of modified 9Cr–1MoVNb steel plates under SMA and GTA welding processes
  publication-title: Trans. Iron Steel Inst. Jpn.
– volume: 115
  start-page: 1
  year: 2016
  end-page: 8
  article-title: Impact resistance and hardness modelling of aluminium alloy welds using square‐headed friction‐stir welding tool
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
– volume: 346
  start-page: 1
  year: 2018
  end-page: 8
  article-title: Prediction analysis of weld‐bead and heat affected zone in TIG welding using artificial neural networks
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
– volume: 32
  start-page: 1931
  year: 2011
  end-page: 1939
  article-title: Effects of preheating temperature on cold cracks, microstructures and properties of high power laser hybrid welded 10Ni3CrMoV steel
  publication-title: Mater. Des.
– volume: 41
  start-page: 549
  issue: 5
  year: 2016
  end-page: 559
  article-title: A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged‐arc welding
  publication-title: Indian Acad. Sci.
– volume: 103
  start-page: 1
  year: 2015
  end-page: 15
  article-title: Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075‐T6 aluminum alloy
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
– year: 2014
  article-title: Effect of flux cored arc welding process parameters on bead geometry in super duplex stainless steel claddings
  publication-title: Measurement
– volume: 2013
  start-page: 1
  year: 2013
  end-page: 8
  article-title: Sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass
  publication-title: Adv. Mater. Sci. Eng.
– volume: 528
  start-page: 8031
  issue: 27
  year: 2011
  end-page: 8039
  article-title: Improvement of impact toughness of AWS E6010 weld metal by adding TiO nanoparticles to the electrode coating
  publication-title: Mater. Sci. Eng. A
– volume: 12
  start-page: 490
  issue: 6
  year: 2016
  end-page: 495
  article-title: Study on hydrogen assisted cracking susceptibility of HSLA steel by implant test
  publication-title: Def. Technol.
– volume: 721
  start-page: 38
  year: 2018
  end-page: 46
  article-title: Effect of niobium precipitation behavior on microstructure and hydrogen induced cracking of press hardening steel 22MnB5
  publication-title: Mater. Sci. Eng. A
– volume: 44
  start-page: 370
  year: 2016
  end-page: 382
  article-title: Effect of nanomaterial addition using GMAW and GTAW processes
  publication-title: Rev. Adv. Mater. Sci.
– year: 2004
– volume: 19
  start-page: 473
  issue: 5
  year: 2010
  end-page: 479
  article-title: Application of Taguchi method for the optimisation of system parameters of centrifugal evaporative air cooler
  publication-title: J. Therm. Sci.
– volume: 528
  start-page: 3350
  year: 2011
  end-page: 3357
  article-title: Effects of acicular ferrite on charpy impact properties in heat affected zones of oxide‐containing API X80 line pipe steels
  publication-title: Mater. Sci. Eng. A
– volume: 95
  start-page: 70
  year: 2017
  end-page: 76
  article-title: Modeling of the microstructural properties of ( )ZnO(1 −  )Fe O nanocrystalline by artificial neural network and response surface methodology
  publication-title: Measurement
– volume: 53
  start-page: 778
  issue: 11
  year: 2015
  end-page: 784
  article-title: Improvement of weld metal fracture toughness by micro‐addition of rare earth metal
  publication-title: Korean J. Met. Mater.
– volume: 23
  start-page: 1995
  issue: 5
  year: 2012
  end-page: 2001
  article-title: Predicting the depth of penetration and weld bead width from the infrared thermal image of the weld pool using artificial neural network modeling
  publication-title: J. Intell. Manuf.
– volume: 94
  start-page: 764
  issue: 12
  year: 2014
  end-page: 771
  article-title: Hydrogen‐ induced cold cracking in heat‐affected zone of low‐carbon high‐strength steel
  publication-title: Philos. Mag. Lett.
– volume: 4
  start-page: 94
  issue: 5
  year: 2016
  end-page: 99
  article-title: Enhancement of impact strength of SAW welded low carbon steels by addition of titanium and manganese in agglomerated flux
  publication-title: Int. J. Mech. Prod. Eng.
– volume: 31
  start-page: 1702
  issue: 12
  year: 2016
  end-page: 1710
  article-title: Effects of rare earth oxide Y O on microstructure and mechanical properties of pro‐eutectoid ferrite/granular bainitic coating
  publication-title: Int. J. Mater. Res.
– volume: 62
  start-page: 83
  year: 2014
  end-page: 90
  article-title: Influence of Y on microstructures and mechanical properties of high strength steel weld metal
  publication-title: Mater. Des.
– volume: 9
  start-page: 229
  issue: 3
  year: 2004
  end-page: 236
  article-title: Influence of pre‐ and post‐weld heating on weldability of modified 9Cr–1Mo(V–Nb) steel pipe under shielded metal arc and tungsten inert gas welding processes
  publication-title: Sci. Technol. Weld. Joining
– volume: 10
  start-page: 897
  issue: 3
  year: 2010
  end-page: 907
  article-title: Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process
  publication-title: Appl. Soft Comput.
– volume: 44
  start-page: 1201
  issue: 7
  year: 2004
  ident: e_1_2_8_12_1
  article-title: Influence of pre and post weld heating on weldability of modified 9Cr–1MoVNb steel plates under SMA and GTA welding processes
  publication-title: Trans. Iron Steel Inst. Jpn.
  doi: 10.2355/isijinternational.44.1201
  contributor:
    fullname: Ghosh P.K.
– volume: 53
  start-page: 778
  issue: 11
  year: 2015
  ident: e_1_2_8_8_1
  article-title: Improvement of weld metal fracture toughness by micro‐addition of rare earth metal
  publication-title: Korean J. Met. Mater.
  doi: 10.3365/KJMM.2015.53.11.778
  contributor:
    fullname: Bang K.‐S.
– ident: e_1_2_8_17_1
– volume: 12
  start-page: 490
  issue: 6
  year: 2016
  ident: e_1_2_8_9_1
  article-title: Study on hydrogen assisted cracking susceptibility of HSLA steel by implant test
  publication-title: Def. Technol.
  doi: 10.1016/j.dt.2016.09.003
  contributor:
    fullname: Chakraborty G.
– volume: 95
  start-page: 70
  year: 2017
  ident: e_1_2_8_22_1
  article-title: Modeling of the microstructural properties of (x)ZnO(1 − x)Fe2 O3 nanocrystalline by artificial neural network and response surface methodology
  publication-title: Measurement
  doi: 10.1016/j.measurement.2016.09.041
  contributor:
    fullname: Louly M.A.
– volume: 2013
  start-page: 1
  year: 2013
  ident: e_1_2_8_27_1
  article-title: Sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass
  publication-title: Adv. Mater. Sci. Eng.
  doi: 10.1155/2013/574914
  contributor:
    fullname: Shojaeefard M.H.
– volume: 32
  start-page: 1931
  year: 2011
  ident: e_1_2_8_28_1
  article-title: Effects of preheating temperature on cold cracks, microstructures and properties of high power laser hybrid welded 10Ni3CrMoV steel
  publication-title: Mater. Des.
  doi: 10.1016/j.matdes.2010.12.007
  contributor:
    fullname: Hu L.H.
– volume: 41
  start-page: 549
  issue: 5
  year: 2016
  ident: e_1_2_8_23_1
  article-title: A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged‐arc welding
  publication-title: Indian Acad. Sci.
  contributor:
    fullname: Sarkar A.
– volume: 23
  start-page: 1995
  issue: 5
  year: 2012
  ident: e_1_2_8_21_1
  article-title: Predicting the depth of penetration and weld bead width from the infrared thermal image of the weld pool using artificial neural network modeling
  publication-title: J. Intell. Manuf.
  doi: 10.1007/s10845-011-0526-4
  contributor:
    fullname: Chokkalingham S.
– volume: 103
  start-page: 1
  year: 2015
  ident: e_1_2_8_16_1
  article-title: Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075‐T6 aluminum alloy
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
  doi: 10.1088/1757-899X/103/1/012034
  contributor:
    fullname: Maleki E.
– ident: e_1_2_8_2_1
– volume: 528
  start-page: 3350
  year: 2011
  ident: e_1_2_8_3_1
  article-title: Effects of acicular ferrite on charpy impact properties in heat affected zones of oxide‐containing API X80 line pipe steels
  publication-title: Mater. Sci. Eng. A
  doi: 10.1016/j.msea.2011.01.031
  contributor:
    fullname: Sung H.K.
– volume: 721
  start-page: 38
  year: 2018
  ident: e_1_2_8_5_1
  article-title: Effect of niobium precipitation behavior on microstructure and hydrogen induced cracking of press hardening steel 22MnB5
  publication-title: Mater. Sci. Eng. A
  doi: 10.1016/j.msea.2018.02.021
  contributor:
    fullname: Lina L.
– volume: 10
  start-page: 897
  issue: 3
  year: 2010
  ident: e_1_2_8_25_1
  article-title: Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2009.10.007
  contributor:
    fullname: Nagesh D.
– volume: 115
  start-page: 1
  year: 2016
  ident: e_1_2_8_15_1
  article-title: Impact resistance and hardness modelling of aluminium alloy welds using square‐headed friction‐stir welding tool
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
  doi: 10.1088/1757-899X/115/1/012002
  contributor:
    fullname: Sudhakar U.
– volume: 14
  start-page: 417
  year: 2013
  ident: e_1_2_8_13_1
  article-title: Prevention of cold cracking in ultra‐high‐strength steel Weldox 1300
  publication-title: Arch. Civ. Mech. Eng.
  doi: 10.1016/j.acme.2013.10.010
  contributor:
    fullname: Węglowski M.St.
– volume: 528
  start-page: 8031
  issue: 27
  year: 2011
  ident: e_1_2_8_4_1
  article-title: Improvement of impact toughness of AWS E6010 weld metal by adding TiO2 nanoparticles to the electrode coating
  publication-title: Mater. Sci. Eng. A
  doi: 10.1016/j.msea.2011.07.035
  contributor:
    fullname: Fattahi M.N.
– volume: 31
  start-page: 1702
  issue: 12
  year: 2016
  ident: e_1_2_8_10_1
  article-title: Effects of rare earth oxide Y2 O3 on microstructure and mechanical properties of pro‐eutectoid ferrite/granular bainitic coating
  publication-title: Int. J. Mater. Res.
  doi: 10.1557/jmr.2016.176
  contributor:
    fullname: Xing X.
– ident: e_1_2_8_30_1
– volume: 551
  start-page: 40
  year: 2012
  ident: e_1_2_8_6_1
  article-title: Effect of manganese content and microstructure on the susceptibility of X70 pipeline steel to hydrogen cracking
  publication-title: Mater. Sci. Eng. A
  doi: 10.1016/j.msea.2012.04.076
  contributor:
    fullname: Hejazi D.
– volume: 346
  start-page: 1
  year: 2018
  ident: e_1_2_8_20_1
  article-title: Prediction analysis of weld‐bead and heat affected zone in TIG welding using artificial neural networks
  publication-title: IOP Conf. Ser., Mater. Sci. Eng.
  contributor:
    fullname: Shamith L.S.
– volume: 44
  start-page: 370
  year: 2016
  ident: e_1_2_8_11_1
  article-title: Effect of nanomaterial addition using GMAW and GTAW processes
  publication-title: Rev. Adv. Mater. Sci.
  contributor:
    fullname: Vimalraj C.
– volume: 19
  start-page: 473
  issue: 5
  year: 2010
  ident: e_1_2_8_24_1
  article-title: Application of Taguchi method for the optimisation of system parameters of centrifugal evaporative air cooler
  publication-title: J. Therm. Sci.
  doi: 10.1007/s11630-010-0411-z
  contributor:
    fullname: Senthilkumar K.
– volume: 62
  start-page: 83
  year: 2014
  ident: e_1_2_8_7_1
  article-title: Influence of Y on microstructures and mechanical properties of high strength steel weld metal
  publication-title: Mater. Des.
  doi: 10.1016/j.matdes.2014.02.057
  contributor:
    fullname: Cai Y.C.
– year: 2014
  ident: e_1_2_8_26_1
  article-title: Effect of flux cored arc welding process parameters on bead geometry in super duplex stainless steel claddings
  publication-title: Measurement
  contributor:
    fullname: Senthilkumar B.
– volume: 94
  start-page: 764
  issue: 12
  year: 2014
  ident: e_1_2_8_29_1
  article-title: Hydrogen‐ induced cold cracking in heat‐affected zone of low‐carbon high‐strength steel
  publication-title: Philos. Mag. Lett.
  doi: 10.1080/09500839.2014.976286
  contributor:
    fullname: Lan L.
– volume: 4
  start-page: 94
  issue: 5
  year: 2016
  ident: e_1_2_8_19_1
  article-title: Enhancement of impact strength of SAW welded low carbon steels by addition of titanium and manganese in agglomerated flux
  publication-title: Int. J. Mech. Prod. Eng.
  contributor:
    fullname: Chandra R.K.
– volume: 9
  start-page: 229
  issue: 3
  year: 2004
  ident: e_1_2_8_14_1
  article-title: Influence of pre‐ and post‐weld heating on weldability of modified 9Cr–1Mo(V–Nb) steel pipe under shielded metal arc and tungsten inert gas welding processes
  publication-title: Sci. Technol. Weld. Joining
  doi: 10.1179/136217104225012265
  contributor:
    fullname: Ghosh P.K.
– volume: 67
  start-page: 32
  issue: 1
  year: 2013
  ident: e_1_2_8_18_1
  article-title: Role of artificial neural network in welding technology: a survey
  publication-title: Int. J. Comput. Appl.
  contributor:
    fullname: Kumar A.
SSID ssj0001342041
Score 2.1371613
Snippet The objective of the study is to predict the cold cracking resistance of high strength low alloy 950A welded joints using an artificial neural network (ANN)...
SourceID doaj
crossref
wiley
iet
SourceType Open Website
Aggregation Database
Publisher
StartPage 447
SubjectTerms alloy steel
ANN
arc welding
artificial neural network model
artificial neural network modelling
cold cracking resistance
cold-crack resistance
cracks
current 950.0 A
experimental data
gas metal arc welding process
given input parameters
heat input
high strength low
identified process parameters
impact strength
neural nets
oxide particle content
oxide particles content
plate welding
plates (structures)
predicted observed values
preheating temperature
propagation model
Research Article
sensitivity analysis
weld metal
welded joints
welding
welds
SummonAdditionalLinks – databaseName: Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T-QwELYQFRSI14mFA1kI0YV1_EjiEhArREEFEp2VsSf3AO0iWHS6f38zTkBLcdBQRbKtZPR9SWbGj2-EOPLoQDvUBSUXbWGhrovWoC9abyrEqMCnrPZ5XV3e2qs7d7dQ6ov3hPXywD1wY7TQYDI8XectJGzKuuTFuS5BS_6pT3xKt5BM5dkVY7Wy5SCqSVHw-PeMRTHL5sTpun7nhLJWP7mWXzh_H6FmFzNZF2tDbChPe5s2xBJON8XqgmLglgDu7EUfJEtR5kveyC1zTRs-XC5nnSR6UxGf2ngvKZ_mGJHI5Q6WJ5Z8QGT6Y_5TPsz-SF55_0tNiA_SO3W6LW4nFzfnl8VQJqGIRnn6RdgulR0BE1XVdFAieWjXUDhqKqs6pZKvVWojGNu4aEvW3IuGfBSk6MEZZ76J5elsijtCxqhThUq3TTTWggbwUCfQHqPvKFEZieNX3MJjr4YR8iq29YEADgxwYIBH4oxRfRvEIta5gagNA7XhM2pH4pA4CcNH9fy_J40zZR_bE26uLvTZJAvm7X6FbXtihW7u-_3b38Xy_OkF9yk8mcNBfhP_AeGo3uc
  priority: 102
  providerName: Directory of Open Access Journals
– databaseName: Wiley Online Library Open Access
  dbid: 24P
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwELZoudBDxatiy0MWQtzSdfxI4mOLuqp6QBy60t6sjD3ZVlSbalmEuPET-I38Emac0LKHInGKZCdOMuPxfOPHN0K88-hAO9QFBRdtYaGui9agL1pvKsSowKfM9vmxOpvb84VbjHlO-SzMwA9xO-HGlpHHazbwFoYsJARq2ch7ZrksmyOn63pHPCRo03C31vbT3SSLsVrl7JWaOl9BsbsbeTapjelWC1t-KdP3k7e5ws02aM1eZ_ZY7I9wUR4P-n0iHuDqqdj7i0TwmUhcOfBASGanzJe8t1vmNDd83lz2nSSNp18_fsZ1Gz9LCrIZOJLGuYo5iyWfGlktN5fyuv8meTn-OxUhXkvv1PFzMZ-dXnw4K8bcCUU0ytO4YbtUdtiUUVVNByWS23YNYVRTWdUplXytUhvB2MZFWzIRXzTkuCBFD844cyB2V_0KXwgZo04VKt020VgLGsBDnUB7jL6j6GUi3v-RXLgZKDJCXtq2PpCIA4s4sIgn4oTlensTM1vngn69DKOhBLTQYDI8PestJPqDuuTF2C5BS3iknIi3pJUwWtqX-940zUr79_eEi_NTfTLLLHqH__3ES_GIyv2wg_uV2N2sv-JrAigbeJM74W9OXNzA
  priority: 102
  providerName: Wiley-Blackwell
Title Artificial neural network modelling of cold-crack resistance of high strength low alloy steel 950A
URI http://digital-library.theiet.org/content/journals/10.1049/joe.2018.5277
https://onlinelibrary.wiley.com/doi/abs/10.1049%2Fjoe.2018.5277
https://doaj.org/article/e4b8ed3b01394bde81712870fdba0211
Volume 2019
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1La9wwEB6S7KU9lKQPuk2yiFJ6cyPrYVvHzWNJQyk9ZEvoRVjSOC0JuyFsKfn3mZG9paE09GQj2ZY9I2u-GUnfALxzaIOyqApyLtrChLouWo2uaJ2uEKMMLmW2z8_V6dycXdiLDVhnG00_LjlXRrGOuHG0HPudB7x0m8bhg0HGfUISwrdUwHSXZfPBqrrehJGiLkZu2Ojj8afBvcrxFm2UNOVAs_nXfQ_MUmbvJ2NDTT_ErNnozLbh2YAWxbRX7w5s4OI5PP2DQ_AFBK7saSAEk1PmQ17aLXKWG95uLpadIIWnIt628UqQh82okdTNFUxYLHjLyOJy9V1cL38Jnou_oyLEa-GsnL6E-ezk_Oi0GBInFFFLR4OG6VLZYVNGWTVdKJFstm0IoOrKyE7K5GqZ2hi0aWw0JbPwRU1WK6TogtVWv4KtxXKBr0HEqFKFUrVN1MYEFYILdQrKYXQduS5jeL-Wm7_p-TF8ntc2zpOAPQvYs4DHcMhS_X0R01rnAtKtH_4SjyY0mDTHZp0Jib6gLnkmtkuhJTBSjuEt6cSvu8C_WjrIKnv8ffz52Yk6nGUKvTf_89hdeELnrl-xvQdbq9ufuE-AZBUmsKnMlwmMpl_n3-aT7NZPhv53D34b3qU
link.rule.ids 315,786,790,870,2115,11589,27947,27955,27956,46085,46509,50847,50956
linkProvider Institution of Engineering and Technology
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlZ1LT9wwEIAtCoe2B1T6ULe8rKrqLcXxI4mPgFgtT_WwSNys2J7wKNpU262q3voT-I38EmacFNgDlXqK5DhOMmN7Zvz4zNgnC8ZLAzLD4KLOtC_LrFZgs9qqAiAIb2OifZ4Uo1N9cGbOHu3i7_gQ9wNu1DJSf00NnAaku4BTEyTzqiXMZV59MbIsn7ElTTA4Yjvrrw-jLEpLkY6vlFj7MgzeTQ_axDK25kqYM0yJ34_m5hJm815rMjvDV2y59xf5dqfgFbYAk9fs5SOK4BsW6WYHguCEp0yXtLibp3NuaMM5bxuOKo-3f27CtA7fOEbZ5DmiyukWQYs5bRuZnM8u-HX7i9N8_G9MArjm1ojtt-x0uDfeHWX94QlZUMJix6GbmDdQ5UEUVeNzQLttKnRSVaFFI0S0pYh18EpXJuicSHxBoeXyMVhvlFHv2OKkncB7xkOQsQAh6yoorb303voyemkh2AbDlwH7_Fdy7nvHyHBpbltbhyJ2JGJHIh6wHZLrfSZCW6eEdnru-pbiQPsKoqLxWat9xD8oc5qNbaKv0SHJB-wjasX1Te3HU2_aSkr79_e48cGe3BkmjN6H_35ikz0fjY-P3NH-yeEqe4F5bLece40tzqY_YR29lZnfSBXyDj1y4Cg
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwELZaKlXlgOgDsVCoVVW9pTh-JPERCitKK8QBJG5WbE-WFrRByyLEjZ_Ab-SXMOOklD2A1FMkx3GSGY_nGz--YeyLBeOlAZlhcFFn2pdlViuwWW1VARCEtzGxfe4Xu0d679gc93lO6SxMxw_xMOFGlpHGazLw89h08aYmjsw_LbFc5tU3I8vyJXulC0QPRO2sD_5NsigtRcpeKbHzZRi7m55nE9vYmGlhxi8l-n70Nr9hOgtak9cZLrKFHi7yzU6_b9kLGL9j849IBN-zSDc7HghO7JTpkvZ285Tmhs6b87bhqPF4d3MbJnU45RhkE3BEjdMt4izmdGpkPJqe8LP2itNy_DUWAZxxa8TmB3Y03Dn8vpv1uROyoITFcUM3MW-gyoMoqsbngG7bVIhRVaFFI0S0pYh18EpXJuiciPiCQsflY7DeKKOW2Ny4HcMy4yHIWICQdRWU1l56b30ZvbQQbIPRy4B9_Ss5d95RZLi0tK2tQxE7ErEjEQ_YFsn1oRIxW6eCdjJyvaE40L6CqGh61mof8Q_KnBZjm-hrxCP5gH1Grbje0i6eetNGUtrz3-MO93bk1jCx6K389xOf2OuD7aH79WP_5yp7g1Vst5n7I5ubTi5hDbHK1K-n_ngP7bPfUQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Artificial+neural+network+modelling+of+cold-crack+resistance+of+high+strength+low+alloy+steel+950A&rft.jtitle=Journal+of+engineering+%28Stevenage%2C+England%29&rft.au=Manivelmuralidaran%2C+Velumani&rft.au=Senthilkumar%2C+Krishnasamy&rft.date=2019-02-01&rft.pub=The+Institution+of+Engineering+and+Technology&rft.eissn=2051-3305&rft.volume=2019&rft.issue=2&rft.spage=447&rft.epage=454&rft_id=info:doi/10.1049%2Fjoe.2018.5277&rft.externalDocID=10_1049_joe_2018_5277
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2051-3305&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2051-3305&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2051-3305&client=summon