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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ali, M. A. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Society, American Welding
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2024Multiphysics Simulation of In-Service Welding and Induction Preheating: Part 2citations
- 2023Corrosion Resistance of Dissimilar GTA Welds for Offshore Applicationscitations
- 2023Application of Machine Learning to Regression Analysis of a Large SMA Weld Metal Databasecitations
- 2023Application of Digital Image Correlation in Cross Weld Tensile Testing: Test Method Validationcitations
- 2022The Toughness of High-Strength Steel Weld Metalscitations
- 2022Metallurgical Design Rules for High-Strength Steel Weld Metalscitations
- 2021Analysis of a High-Strength Steel SMAW Databasecitations
- 2020Steel-Reinforced Polyethylene Pipe: Extrusion Welding, Investigation, and Mechanical Testingcitations
- 2020Metal Transfer Mechanisms in Hot-Wire Gas Metal Arc Weldingcitations
- 2020Effect of PWHT on Laser-Welded Duplex Stainless Steelcitations
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article
Application of Machine Learning to Regression Analysis of a Large SMA Weld Metal Database
Abstract
<jats:p>A machine learning approach was used to perform a regression analysis of Evans’s shielded metal arc (SMA) weld metal (WM) database involving several groups of Fe-C-Mn high-strength steels. The objective of this investigation was to develop an expression for austenite-to-ferrite (Ar3) transformation temperature that also included the effects of principal and minor alloy elements (in wt-%) and weld cooling rate (in °C/s) and relate this expression with WM ultimate tensile strength (UTS). The Ar3 data from 257 records obtained from several selected sources were combined with Ar3 projections at extreme end points in Evans’s WM database.Subsequently, a cluster analysis was performed. The data in Evans’s database was filtered with the carbon equivalent number limited to 0.3 maximum, carbon content limited to 0.1 wt-% maximum, nitrogen content limited to 99 ppm (0.0099 wt-%) maximum, preassigned Ar3 values limited to 680°C minimum, and WM UTS limited to 710 MPa maximum. The results provided a good approximation to the expression for Ar3 transformation temperature in terms of elemental compositions and cooling rate. This allowed the Ar3 to correlate with WM UTS of Fe-C-Mn in at least four ways depending on the sign of correlation of the data clusters.The elemental combinations in the cluster with the highest negative correlation revealed highly predictable WM UTS. In particular, the new Ar3 expression helped to predict decreases observed in certain Ar3 experimental data on WMs with balanced Ti, B, Al, N, and O additions reported among 13 records with additional dilatometry results.This correlation between the new expression for the Ar3 temperature and UTS of Fe-C-Mn WM is expected to complement the Japan Welding Engineering Society artificial neural network model currently available to predict Charpy V-notch test temperature for 28 J absorbed energy based on WM chemical composition. It will thereby provide a pair of effective tools for efficient development and/or evaluation of high-performance welding electrodes based on an Fe-C-Mn system for demand-critical applications.</jats:p>