Fatigue Life Prediction of 2024-T3 Al Alloy by Integrating Particle Swarm Optimization-Extreme Gradient Boosting and Physical Model
The multi-parameter characteristics of the physical model pose a challenge to the fatigue life prediction of 2024-T3 aluminum (Al) alloy. In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with extreme gradient boosting (XGBoost) is proposed in th...
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| Veröffentlicht in: | Materials 2024-10, Vol.17 (21), p.5332 |
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| creator | Li, Zhaoji Yue, Haitao Zhang, Ce Dai, Weibing Guo, Chenguang Li, Qiang Zhang, Jianzhuo |
| description | The multi-parameter characteristics of the physical model pose a challenge to the fatigue life prediction of 2024-T3 aluminum (Al) alloy. In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with extreme gradient boosting (XGBoost) is proposed in this study. The fatigue performance and failure mechanism of the 2024-T3 Al alloy are analyzed. Furthermore, the fatigue life prediction physical model of the 2024-T3 Al alloy is established by using the energy method of fracture mechanics. The physical model incorporates critical physical parameters. Meanwhile, the PSO algorithm optimizes the hyperparameters of the XGBoost model based on fatigue data of the 2024-T3 Al alloy. Eventually, the optimized XGBoost model is used to solve the parameters of the physical model. Furthermore, the analytical equation of the fatigue life prediction model is obtained. This paper provides a new method for solving the parameters of the fatigue life prediction model, which reduces the error and cost of obtaining the model parameters in the experiment and shortens the time required. |
| doi_str_mv | 10.3390/ma17215332 |
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In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with extreme gradient boosting (XGBoost) is proposed in this study. The fatigue performance and failure mechanism of the 2024-T3 Al alloy are analyzed. Furthermore, the fatigue life prediction physical model of the 2024-T3 Al alloy is established by using the energy method of fracture mechanics. The physical model incorporates critical physical parameters. Meanwhile, the PSO algorithm optimizes the hyperparameters of the XGBoost model based on fatigue data of the 2024-T3 Al alloy. Eventually, the optimized XGBoost model is used to solve the parameters of the physical model. Furthermore, the analytical equation of the fatigue life prediction model is obtained. This paper provides a new method for solving the parameters of the fatigue life prediction model, which reduces the error and cost of obtaining the model parameters in the experiment and shortens the time required.</description><identifier>ISSN: 1996-1944</identifier><identifier>EISSN: 1996-1944</identifier><identifier>DOI: 10.3390/ma17215332</identifier><identifier>PMID: 39517605</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Alloys ; Aluminum alloys ; Aluminum base alloys ; Analysis ; Crack initiation ; Crack propagation ; Energy methods ; Error analysis ; Failure mechanisms ; Fatigue ; Fatigue failure ; Fatigue life ; Fatigue testing machines ; Fracture mechanics ; Hydraulics ; Life prediction ; Machine learning ; Materials ; Mathematical optimization ; Metal fatigue ; Parameters ; Particle swarm optimization ; Physical properties ; Prediction models ; Research methodology ; Specialty metals industry ; Stress concentration</subject><ispartof>Materials, 2024-10, Vol.17 (21), p.5332</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 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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In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with extreme gradient boosting (XGBoost) is proposed in this study. The fatigue performance and failure mechanism of the 2024-T3 Al alloy are analyzed. Furthermore, the fatigue life prediction physical model of the 2024-T3 Al alloy is established by using the energy method of fracture mechanics. The physical model incorporates critical physical parameters. Meanwhile, the PSO algorithm optimizes the hyperparameters of the XGBoost model based on fatigue data of the 2024-T3 Al alloy. Eventually, the optimized XGBoost model is used to solve the parameters of the physical model. Furthermore, the analytical equation of the fatigue life prediction model is obtained. This paper provides a new method for solving the parameters of the fatigue life prediction model, which reduces the error and cost of obtaining the model parameters in the experiment and shortens the time required.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Alloys</subject><subject>Aluminum alloys</subject><subject>Aluminum base alloys</subject><subject>Analysis</subject><subject>Crack initiation</subject><subject>Crack propagation</subject><subject>Energy methods</subject><subject>Error analysis</subject><subject>Failure mechanisms</subject><subject>Fatigue</subject><subject>Fatigue failure</subject><subject>Fatigue life</subject><subject>Fatigue testing machines</subject><subject>Fracture mechanics</subject><subject>Hydraulics</subject><subject>Life prediction</subject><subject>Machine learning</subject><subject>Materials</subject><subject>Mathematical optimization</subject><subject>Metal fatigue</subject><subject>Parameters</subject><subject>Particle swarm optimization</subject><subject>Physical properties</subject><subject>Prediction models</subject><subject>Research methodology</subject><subject>Specialty metals industry</subject><subject>Stress concentration</subject><issn>1996-1944</issn><issn>1996-1944</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkV1rHCEUhqW0NCHNTX9AEXpTCpP4NR9ebkOSBjZkoen1oM5xa3DGrTqkm9v88brdtA1RQZHnkXN8EXpPyQnnkpyOiraM1pyzV-iQStlUVArx-tn5AB2ndEfK4Jx2TL5FB1zWtG1IfYgeL1R26xnw0lnAqwiDM9mFCQeLGWGiuuV44cvyYYv1Fl9NGdaxONMar1TMznjA3-5VHPHNJrvRPaidXp3_yhFGwJdRDQ6mjL-EkP5Yahrw6sc2OaM8vg4D-HfojVU-wfHTfoS-X5zfnn2tljeXV2eLZWWY4Lnirel0q2oiTUPa0gLnndBaNy3UrJYDpUJZNWij6sYYYUzLpLYMeKMFt6X5I_Rp_-4mhp8zpNyPLhnwXk0Q5tRzyrpWEMpZQT--QO_CHKdS3Y5qCGu6Zked7Km18tC7yYYclSlzgNGZMIF15X7R0VqUv5S0CJ_3gokhpQi230Q3qrjtKel3cfb_4yzwh6caZj3C8A_9Gx7_DeLTmIg</recordid><startdate>20241031</startdate><enddate>20241031</enddate><creator>Li, Zhaoji</creator><creator>Yue, Haitao</creator><creator>Zhang, Ce</creator><creator>Dai, Weibing</creator><creator>Guo, Chenguang</creator><creator>Li, Qiang</creator><creator>Zhang, Jianzhuo</creator><general>MDPI AG</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><orcidid>https://orcid.org/0000-0002-0900-5647</orcidid><orcidid>https://orcid.org/0000-0002-0834-5287</orcidid></search><sort><creationdate>20241031</creationdate><title>Fatigue Life Prediction of 2024-T3 Al Alloy by Integrating Particle Swarm Optimization-Extreme Gradient Boosting and Physical Model</title><author>Li, Zhaoji ; Yue, Haitao ; Zhang, Ce ; Dai, Weibing ; Guo, Chenguang ; Li, Qiang ; Zhang, Jianzhuo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c243t-37c8b7a509c6073953384bbb67e5259d114afadbca56cc4cc729bf2e36b43f033</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Alloys</topic><topic>Aluminum alloys</topic><topic>Aluminum base alloys</topic><topic>Analysis</topic><topic>Crack initiation</topic><topic>Crack propagation</topic><topic>Energy methods</topic><topic>Error analysis</topic><topic>Failure mechanisms</topic><topic>Fatigue</topic><topic>Fatigue failure</topic><topic>Fatigue life</topic><topic>Fatigue testing machines</topic><topic>Fracture mechanics</topic><topic>Hydraulics</topic><topic>Life prediction</topic><topic>Machine learning</topic><topic>Materials</topic><topic>Mathematical optimization</topic><topic>Metal fatigue</topic><topic>Parameters</topic><topic>Particle swarm optimization</topic><topic>Physical properties</topic><topic>Prediction models</topic><topic>Research methodology</topic><topic>Specialty metals industry</topic><topic>Stress concentration</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Zhaoji</creatorcontrib><creatorcontrib>Yue, Haitao</creatorcontrib><creatorcontrib>Zhang, Ce</creatorcontrib><creatorcontrib>Dai, Weibing</creatorcontrib><creatorcontrib>Guo, Chenguang</creatorcontrib><creatorcontrib>Li, Qiang</creatorcontrib><creatorcontrib>Zhang, Jianzhuo</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>Materials Science Database</collection><collection>Materials Science Collection</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>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Materials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Zhaoji</au><au>Yue, Haitao</au><au>Zhang, Ce</au><au>Dai, Weibing</au><au>Guo, Chenguang</au><au>Li, Qiang</au><au>Zhang, Jianzhuo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fatigue Life Prediction of 2024-T3 Al Alloy by Integrating Particle Swarm Optimization-Extreme Gradient Boosting and Physical Model</atitle><jtitle>Materials</jtitle><addtitle>Materials (Basel)</addtitle><date>2024-10-31</date><risdate>2024</risdate><volume>17</volume><issue>21</issue><spage>5332</spage><pages>5332-</pages><issn>1996-1944</issn><eissn>1996-1944</eissn><abstract>The multi-parameter characteristics of the physical model pose a challenge to the fatigue life prediction of 2024-T3 aluminum (Al) alloy. In response to this issue, a parameter-solving method that integrates particle swarm optimization (PSO) with extreme gradient boosting (XGBoost) is proposed in this study. The fatigue performance and failure mechanism of the 2024-T3 Al alloy are analyzed. Furthermore, the fatigue life prediction physical model of the 2024-T3 Al alloy is established by using the energy method of fracture mechanics. The physical model incorporates critical physical parameters. Meanwhile, the PSO algorithm optimizes the hyperparameters of the XGBoost model based on fatigue data of the 2024-T3 Al alloy. Eventually, the optimized XGBoost model is used to solve the parameters of the physical model. Furthermore, the analytical equation of the fatigue life prediction model is obtained. 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| subjects | Accuracy Algorithms Alloys Aluminum alloys Aluminum base alloys Analysis Crack initiation Crack propagation Energy methods Error analysis Failure mechanisms Fatigue Fatigue failure Fatigue life Fatigue testing machines Fracture mechanics Hydraulics Life prediction Machine learning Materials Mathematical optimization Metal fatigue Parameters Particle swarm optimization Physical properties Prediction models Research methodology Specialty metals industry Stress concentration |
| title | Fatigue Life Prediction of 2024-T3 Al Alloy by Integrating Particle Swarm Optimization-Extreme Gradient Boosting and Physical Model |
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