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Applying Statistical Models to Optimize the Weld Bead Geometry in the Vertical Oscillation Arc Narrow Gap All-Position GMAW
Vertical oscillation arc welding for narrow gap gas metal arc welding (NG-GMAW) has a relatively simple structure, and it is widely used in all-position pipeline field welding. However, it has some shortcomings, such as incomplete fusion defects on the sidewall and interlayer. Aiming at resolving th...
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Published in: | Applied sciences 2023-06, Vol.13 (11), p.6801 |
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description | Vertical oscillation arc welding for narrow gap gas metal arc welding (NG-GMAW) has a relatively simple structure, and it is widely used in all-position pipeline field welding. However, it has some shortcomings, such as incomplete fusion defects on the sidewall and interlayer. Aiming at resolving these shortcomings, a mathematical model is proposed to obtain appropriate welding parameters in different positions. In this model, the response surface methodology (RSM) based on the central composite design (CCD) was developed to study the interactions between welding parameters and the weld bead geometry. Then the analysis of variance (ANOVA) was used to evaluate the accuracy and significance of the proposed model. Finally, experiments were carried out in flat, vertical, and overhead positions to obtain the optimal parameters. The macroscopic metallography of the transversal section of the weld bead under the optimizing welding parameters showed that the weld beads were free of defects in the sidewall and interlayers. |
doi_str_mv | 10.3390/app13116801 |
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However, it has some shortcomings, such as incomplete fusion defects on the sidewall and interlayer. Aiming at resolving these shortcomings, a mathematical model is proposed to obtain appropriate welding parameters in different positions. In this model, the response surface methodology (RSM) based on the central composite design (CCD) was developed to study the interactions between welding parameters and the weld bead geometry. Then the analysis of variance (ANOVA) was used to evaluate the accuracy and significance of the proposed model. Finally, experiments were carried out in flat, vertical, and overhead positions to obtain the optimal parameters. The macroscopic metallography of the transversal section of the weld bead under the optimizing welding parameters showed that the weld beads were free of defects in the sidewall and interlayers.</description><identifier>ISSN: 2076-3417</identifier><identifier>EISSN: 2076-3417</identifier><identifier>DOI: 10.3390/app13116801</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>all-position welding ; Analysis ; Experiments ; Geometry ; incomplete fusion defects ; Interlayers ; Lasers ; Mathematical models ; Metallography ; Methods ; Morphology ; Neural networks ; NG-GMAW ; Response surface methodology ; RSM ; Statistical analysis ; statistical model ; Statistical models ; Variance analysis ; Vertical orientation ; vertical oscillation arc ; Vertical oscillations ; Welding ; Welding parameters</subject><ispartof>Applied sciences, 2023-06, Vol.13 (11), p.6801</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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The macroscopic metallography of the transversal section of the weld bead under the optimizing welding parameters showed that the weld beads were free of defects in the sidewall and interlayers.</description><subject>all-position welding</subject><subject>Analysis</subject><subject>Experiments</subject><subject>Geometry</subject><subject>incomplete fusion defects</subject><subject>Interlayers</subject><subject>Lasers</subject><subject>Mathematical models</subject><subject>Metallography</subject><subject>Methods</subject><subject>Morphology</subject><subject>Neural networks</subject><subject>NG-GMAW</subject><subject>Response surface methodology</subject><subject>RSM</subject><subject>Statistical analysis</subject><subject>statistical model</subject><subject>Statistical models</subject><subject>Variance analysis</subject><subject>Vertical orientation</subject><subject>vertical oscillation arc</subject><subject>Vertical oscillations</subject><subject>Welding</subject><subject>Welding parameters</subject><issn>2076-3417</issn><issn>2076-3417</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkV1rFTEQhhdRsNRe-QcCXsrWfCd7uRY9FlqP4Ecvl2wye8whZ7MmKeXonzc9K9KZixlm5n14YZrmNcGXjHX4nVkWwgiRGpNnzRnFSraME_X8Sf-yuch5j2t0hGmCz5o__bKEo5936GsxxefirQnoNjoIGZWItkvxB_8bUPkJ6A6CQ-_BOLSBeICSjsjPp80PSKtym60PoZLijPpk0WeTUnxAG7OgPoT2S8z-tNvc9nevmheTCRku_tXz5vvHD9-uPrU32831VX_TWo5ZaSknWoCWkxSsI6N0DiYYLVeYYYFlB06KaeSMmFEIBsRYy4UWCgtQhGrMzpvrleui2Q9L8geTjkM0fjgNYtoN5tF-gIEr5ZyeQMlOckq6EY_MaKmmWrjSurLerKwlxV_3kMuwj_dprvYHqinrtGSK1KvL9WpnKtTPUyzJ2JoODt7GGSZf570SVFFOJa2Ct6vApphzgum_TYKHx-8OT77L_gI_dpVV</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Liu, Hongsheng</creator><creator>Xue, Ruilei</creator><creator>Zhou, Jianping</creator><creator>Bao, Yang</creator><creator>Xu, Yan</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4139-2309</orcidid><orcidid>https://orcid.org/0000-0002-0880-1131</orcidid></search><sort><creationdate>20230601</creationdate><title>Applying Statistical Models to Optimize the Weld Bead Geometry in the Vertical Oscillation Arc Narrow Gap All-Position GMAW</title><author>Liu, Hongsheng ; Xue, Ruilei ; Zhou, Jianping ; Bao, Yang ; Xu, Yan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c403t-24185e86f65391b6ddefebc470305069ed65fb431ab553e1acc4585705e712803</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>all-position welding</topic><topic>Analysis</topic><topic>Experiments</topic><topic>Geometry</topic><topic>incomplete fusion defects</topic><topic>Interlayers</topic><topic>Lasers</topic><topic>Mathematical models</topic><topic>Metallography</topic><topic>Methods</topic><topic>Morphology</topic><topic>Neural networks</topic><topic>NG-GMAW</topic><topic>Response surface methodology</topic><topic>RSM</topic><topic>Statistical analysis</topic><topic>statistical model</topic><topic>Statistical models</topic><topic>Variance analysis</topic><topic>Vertical orientation</topic><topic>vertical oscillation arc</topic><topic>Vertical oscillations</topic><topic>Welding</topic><topic>Welding parameters</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Hongsheng</creatorcontrib><creatorcontrib>Xue, Ruilei</creatorcontrib><creatorcontrib>Zhou, Jianping</creatorcontrib><creatorcontrib>Bao, Yang</creatorcontrib><creatorcontrib>Xu, Yan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Databases</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>DOAJ: Directory of Open Access Journals</collection><jtitle>Applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Hongsheng</au><au>Xue, Ruilei</au><au>Zhou, Jianping</au><au>Bao, Yang</au><au>Xu, Yan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applying Statistical Models to Optimize the Weld Bead Geometry in the Vertical Oscillation Arc Narrow Gap All-Position GMAW</atitle><jtitle>Applied sciences</jtitle><date>2023-06-01</date><risdate>2023</risdate><volume>13</volume><issue>11</issue><spage>6801</spage><pages>6801-</pages><issn>2076-3417</issn><eissn>2076-3417</eissn><abstract>Vertical oscillation arc welding for narrow gap gas metal arc welding (NG-GMAW) has a relatively simple structure, and it is widely used in all-position pipeline field welding. However, it has some shortcomings, such as incomplete fusion defects on the sidewall and interlayer. Aiming at resolving these shortcomings, a mathematical model is proposed to obtain appropriate welding parameters in different positions. In this model, the response surface methodology (RSM) based on the central composite design (CCD) was developed to study the interactions between welding parameters and the weld bead geometry. Then the analysis of variance (ANOVA) was used to evaluate the accuracy and significance of the proposed model. Finally, experiments were carried out in flat, vertical, and overhead positions to obtain the optimal parameters. The macroscopic metallography of the transversal section of the weld bead under the optimizing welding parameters showed that the weld beads were free of defects in the sidewall and interlayers.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/app13116801</doi><orcidid>https://orcid.org/0000-0002-4139-2309</orcidid><orcidid>https://orcid.org/0000-0002-0880-1131</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | all-position welding Analysis Experiments Geometry incomplete fusion defects Interlayers Lasers Mathematical models Metallography Methods Morphology Neural networks NG-GMAW Response surface methodology RSM Statistical analysis statistical model Statistical models Variance analysis Vertical orientation vertical oscillation arc Vertical oscillations Welding Welding parameters |
title | Applying Statistical Models to Optimize the Weld Bead Geometry in the Vertical Oscillation Arc Narrow Gap All-Position GMAW |
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