Neural Networks Applied for Predictive Parameters Analysis of the Refill Friction Stir Spot Welding Process of 6061-T6 Aluminum Alloy Plates
Refill friction stir spot welding (RFSSW) technology is a solid-state joint that can replace conventional welding or riveting processes in aerospace applications. The quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed...
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| Veröffentlicht in: | Materials 2023-06, Vol.16 (13), p.4519 |
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| creator | Bîrsan, Dan Cătălin Păunoiu, Viorel Teodor, Virgil Gabriel |
| description | Refill friction stir spot welding (RFSSW) technology is a solid-state joint that can replace conventional welding or riveting processes in aerospace applications. The quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed to understand the complexity of the thermomechanical phenomena during this welding process, validated by controlled experiments. An optimization model using neural networks was developed based on 98 parameter sets resulting from changing 3 welding parameters, namely pin penetration depth, pin rotation speed, and retention time. Ten parameter sets were used to verify the learning results of the optimization model. The 10 results were drawn to correspond to a uniform distribution over the training domain, with the aim of avoiding areas that might have contained distortions. The maximum temperature and normal stress reached at the end of the welding process were considered output data. |
| doi_str_mv | 10.3390/ma16134519 |
| format | Article |
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The quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed to understand the complexity of the thermomechanical phenomena during this welding process, validated by controlled experiments. An optimization model using neural networks was developed based on 98 parameter sets resulting from changing 3 welding parameters, namely pin penetration depth, pin rotation speed, and retention time. Ten parameter sets were used to verify the learning results of the optimization model. The 10 results were drawn to correspond to a uniform distribution over the training domain, with the aim of avoiding areas that might have contained distortions. The maximum temperature and normal stress reached at the end of the welding process were considered output data.</description><identifier>ISSN: 1996-1944</identifier><identifier>EISSN: 1996-1944</identifier><identifier>DOI: 10.3390/ma16134519</identifier><identifier>PMID: 37444833</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Alloys ; Aluminum ; Aluminum alloys ; Aluminum base alloys ; Defects ; Deformation ; Finite element analysis ; Finite element method ; Friction stir welding ; Friction welding ; Innovations ; Manufacturing ; Mechanical properties ; Metal plates ; Metals ; Neural networks ; Normal stress ; Optimization models ; Penetration depth ; Product reliability ; Residual stress ; Riveting ; Shear strength ; Simulation ; Spot welding ; Sustainable development ; Temperature ; Welding ; Welding parameters</subject><ispartof>Materials, 2023-06, Vol.16 (13), p.4519</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 quality of the new welding process is directly influenced by the welding parameters selected. A finite element analysis was performed to understand the complexity of the thermomechanical phenomena during this welding process, validated by controlled experiments. An optimization model using neural networks was developed based on 98 parameter sets resulting from changing 3 welding parameters, namely pin penetration depth, pin rotation speed, and retention time. Ten parameter sets were used to verify the learning results of the optimization model. The 10 results were drawn to correspond to a uniform distribution over the training domain, with the aim of avoiding areas that might have contained distortions. The maximum temperature and normal stress reached at the end of the welding process were considered output data.</description><subject>Alloys</subject><subject>Aluminum</subject><subject>Aluminum alloys</subject><subject>Aluminum base alloys</subject><subject>Defects</subject><subject>Deformation</subject><subject>Finite element analysis</subject><subject>Finite element method</subject><subject>Friction stir welding</subject><subject>Friction welding</subject><subject>Innovations</subject><subject>Manufacturing</subject><subject>Mechanical properties</subject><subject>Metal plates</subject><subject>Metals</subject><subject>Neural networks</subject><subject>Normal stress</subject><subject>Optimization models</subject><subject>Penetration depth</subject><subject>Product reliability</subject><subject>Residual stress</subject><subject>Riveting</subject><subject>Shear strength</subject><subject>Simulation</subject><subject>Spot welding</subject><subject>Sustainable development</subject><subject>Temperature</subject><subject>Welding</subject><subject>Welding parameters</subject><issn>1996-1944</issn><issn>1996-1944</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdklFrFDEQxxdRbKl98QNIwBcRtu5sctnkSY5iq1DqYQ98DNns7DU1u1mTbOW-gx-6uV6t1eQhA_Ob_0z-TFG8huqEUll9GDRwoGwB8llxCFLyEiRjz5_EB8VxjDdVPpSCqOXL4oA2jDFB6WHx-xLnoB25xPTLhx-RLKfJWexI7wNZBeysSfYWyUoHPWDCkIlRu220kfiepGsk37C3zpGzsEP9SK6SDeRq8ol8R9fZcZN1vMF4X8ArDuWak6WbBzvOQw6c35KV0wnjq-JFr13E44f3qFiffVqffi4vvp5_OV1elIYxnkrUrNN1A6KDtmaiE5I3DTDoei6MBllzKrhkBlvWV9Aa2tYLxBoE9E2dk0fFx73sNLcDdgbHlC1QU7CDDlvltVX_ZkZ7rTb-VkFFWS3oIiu8e1AI_ueMManBRoPO6RH9HFWGRJ5sj779D73xc8gW3lOcNbtZM3WypzbaobJj73Njk2-HgzV-3FmMatksBJVM1JAL3u8LTPAxBuwfx4dK7RZD_V2MDL95-uFH9M8a0DuT7LLb</recordid><startdate>20230621</startdate><enddate>20230621</enddate><creator>Bîrsan, Dan Cătălin</creator><creator>Păunoiu, Viorel</creator><creator>Teodor, Virgil Gabriel</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>KB.</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0009-0005-3522-9328</orcidid><orcidid>https://orcid.org/0000-0002-3764-4243</orcidid></search><sort><creationdate>20230621</creationdate><title>Neural Networks Applied for Predictive Parameters Analysis of the Refill Friction Stir Spot Welding Process of 6061-T6 Aluminum Alloy Plates</title><author>Bîrsan, Dan Cătălin ; 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| ispartof | Materials, 2023-06, Vol.16 (13), p.4519 |
| issn | 1996-1944 1996-1944 |
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| source | MDPI - Multidisciplinary Digital Publishing Institute; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; PubMed Central Open Access |
| subjects | Alloys Aluminum Aluminum alloys Aluminum base alloys Defects Deformation Finite element analysis Finite element method Friction stir welding Friction welding Innovations Manufacturing Mechanical properties Metal plates Metals Neural networks Normal stress Optimization models Penetration depth Product reliability Residual stress Riveting Shear strength Simulation Spot welding Sustainable development Temperature Welding Welding parameters |
| title | Neural Networks Applied for Predictive Parameters Analysis of the Refill Friction Stir Spot Welding Process of 6061-T6 Aluminum Alloy Plates |
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