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...
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Published in | Journal of engineering (Stevenage, England) Vol. 2019; no. 2; pp. 447 - 454 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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The Institution of Engineering and Technology
01.02.2019
Wiley |
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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. |
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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 |
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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 |
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Copyright | 2021 The Institution of Engineering and Technology |
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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 |
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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)... |
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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 |
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Title | Artificial neural network modelling of cold-crack resistance of high strength low alloy steel 950A |
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