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Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding
Main drives, cutterheads and other critical components of tunnel shield machines require welding with thick plates that leave roots over 5 mm. Full penetration welds cannot be achieved by conventional Pulsed MAG welding methods. This article introduces Super Spray MAG Welding technology and investig...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256848/ https://www.ncbi.nlm.nih.gov/pubmed/37296279 http://dx.doi.org/10.1038/s41598-023-36505-6 |
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author | Li, Zhifeng Xia, Yimin |
author_facet | Li, Zhifeng Xia, Yimin |
author_sort | Li, Zhifeng |
collection | PubMed |
description | Main drives, cutterheads and other critical components of tunnel shield machines require welding with thick plates that leave roots over 5 mm. Full penetration welds cannot be achieved by conventional Pulsed MAG welding methods. This article introduces Super Spray MAG Welding technology and investigates its penetrating regularities and mechanisms through high-speed camera images, finite element simulation, and microstructural analysis. An optimal welding procedure was generated using a combination of Genetic Algorithm and Back Propagation Neural Network. The data show that Super Spray MAG arc exhibits greater concentration and stability than traditional MAG arc, marking its strong qualities in emitting high-energy beams. The morphological solidification pattern of the molten pool closely matches the FEM simulation results of the composite Gaussian surface heat source model and peak linear attenuation Gaussian cylinder heat source. The welding current mainly affects the penetration of the weld, followed by the extension of the wire, and lastly the welding speed. Increasing the welding current can transition droplet transfer from globular to spray, as well as alter microstructure development and mechanical characteristics. Suggested parameters for penetrating the 5 mm root were put forward. The BPNN-GA model established can effectively predict weld formation, and points out the optimal welding parameters. |
format | Online Article Text |
id | pubmed-10256848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102568482023-06-11 Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding Li, Zhifeng Xia, Yimin Sci Rep Article Main drives, cutterheads and other critical components of tunnel shield machines require welding with thick plates that leave roots over 5 mm. Full penetration welds cannot be achieved by conventional Pulsed MAG welding methods. This article introduces Super Spray MAG Welding technology and investigates its penetrating regularities and mechanisms through high-speed camera images, finite element simulation, and microstructural analysis. An optimal welding procedure was generated using a combination of Genetic Algorithm and Back Propagation Neural Network. The data show that Super Spray MAG arc exhibits greater concentration and stability than traditional MAG arc, marking its strong qualities in emitting high-energy beams. The morphological solidification pattern of the molten pool closely matches the FEM simulation results of the composite Gaussian surface heat source model and peak linear attenuation Gaussian cylinder heat source. The welding current mainly affects the penetration of the weld, followed by the extension of the wire, and lastly the welding speed. Increasing the welding current can transition droplet transfer from globular to spray, as well as alter microstructure development and mechanical characteristics. Suggested parameters for penetrating the 5 mm root were put forward. The BPNN-GA model established can effectively predict weld formation, and points out the optimal welding parameters. Nature Publishing Group UK 2023-06-09 /pmc/articles/PMC10256848/ /pubmed/37296279 http://dx.doi.org/10.1038/s41598-023-36505-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Li, Zhifeng Xia, Yimin Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding |
title | Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding |
title_full | Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding |
title_fullStr | Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding |
title_full_unstemmed | Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding |
title_short | Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding |
title_sort | heat source modeling, penetration analysis and parametric optimization of super spray mag welding |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256848/ https://www.ncbi.nlm.nih.gov/pubmed/37296279 http://dx.doi.org/10.1038/s41598-023-36505-6 |
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