{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:45:12Z","timestamp":1777704312656,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:p>Friction stir welding (FSW) is a complex thermo-mechanical coupling process. Tensile strength is an important evaluation index of the mechanical properties of welded joints. How to realize the real-time prediction of tensile strength of the friction stir welded joints to reflect the dynamic change of welding state is a problem in the field. To solve this problem, this paper presents a multi-scale one-dimensional convolutional neural network (Multi-scale 1D CNN) prediction model using time series data of temperature and axial force as inputs to realize the online prediction of tensile strength of welded joints. Firstly, FSW experiments are carried out to obtain time series data of temperature and axial force. Tensile strength values of the welded joints is obtained by tensile tests. The time series data and tensile strength values are fused as a dataset. Then Multi-scale 1D CNN, traditional 1D CNN and Multi-channel 1D CNN prediction models are established and trained with the dataset, respectively. Finally, by comparing the prediction performance of the three models, Multi-scale 1D CNN is proved to be more suitable for analyzing time series data to feedback the dynamic change of tensile strength of the joints during welding.<\/jats:p>","DOI":"10.3233\/jifs-230144","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T13:20:29Z","timestamp":1685107229000},"page":"2279-2288","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of the tensile strength of friction stir welded joints based on one-dimensional convolutional neural network"],"prefix":"10.1177","volume":"45","author":[{"given":"Xiaohong","family":"Lu","sequence":"first","affiliation":[{"name":"State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Banghua","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shixuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Capital Aerospace Machinery Company, Beijing, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of High-performance Precision Manufacturing, Dalian University of Technology, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-230144_ref1","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.2298\/TSCI210216186V","article-title":"Thermo-mechanical analysis of linear welding stage in friction stir welding Influenceof Welding Parameters[J]","volume":"26","author":"Velji\u0107","year":"2022","journal-title":"Thermal Science"},{"issue":"6","key":"10.3233\/JIFS-230144_ref2","doi-asserted-by":"crossref","first-page":"624","DOI":"10.3390\/met9060624","article-title":"Investigation of nondestructive testing methods for friction stir welding[J]","volume":"9","author":"Taheri","year":"2019","journal-title":"Metals"},{"issue":"11","key":"10.3233\/JIFS-230144_ref3","doi-asserted-by":"crossref","first-page":"4202","DOI":"10.1177\/0954410018816601","article-title":"Study of thermal cycle, mechanical, and metallurgical properties of friction stir welded aviation grade aluminum alloy[J]","volume":"233","author":"Verma","year":"2019","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering"},{"key":"10.3233\/JIFS-230144_ref4","first-page":"128","article-title":"Application of neural network for the prediction of tensile properties of friction stir welded composites[C]\/\/Materials Science Forum","volume":"880","author":"Shettigar","year":"2017","journal-title":"Trans Tech Publications Ltd"},{"issue":"5","key":"10.3233\/JIFS-230144_ref5","first-page":"12124","article-title":"Quality prediction of friction stir weld joints on AA 5052 H32 aluminium alloy using fuzzy logic technique[J]","volume":"5","author":"Shanavas","year":"2018","journal-title":"Materials Today: Proceedings"},{"issue":"2","key":"10.3233\/JIFS-230144_ref6","first-page":"8256","article-title":"Weld quality prediction of AA -H32 aluminium alloy using neural network approach[J]","volume":"5","author":"Shanavas","year":"2018","journal-title":"Materials today: Proceedings"},{"key":"10.3233\/JIFS-230144_ref7","doi-asserted-by":"crossref","unstructured":"Upender K. , Kumar B.V.R.R. , Rao M.S.S. et al. Friction Stir Welding of IS: 65032 Aluminum Alloy and Predicting Tensile Strength Using Ensemble Learning[M]\/\/Advances in Mechanical Engineering and Material Science: Select Proceedings of ICAMEMS-2022, Singapore: Springer Nature Singapore (2022), 103\u2013114.","DOI":"10.1007\/978-981-19-0676-3_8"},{"key":"10.3233\/JIFS-230144_ref8","doi-asserted-by":"crossref","first-page":"3117","DOI":"10.1007\/s00170-017-0897-6","article-title":"Parameter optimization of friction stir welding of cryorolled AAalloy using artificial neural network modeling with genetic algorithm[J]","volume":"94","author":"Babu","year":"2018","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"10.3233\/JIFS-230144_ref9","doi-asserted-by":"crossref","first-page":"79896","DOI":"10.1109\/ACCESS.2020.2990137","article-title":"A Novel Method for Predicting Tensile Strength of Friction Stir Welded AAAluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas 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Strojniski Vestnik\/Journal of Mechanical Engineering 66(6) (2020), 395-407.","DOI":"10.5545\/sv-jme.2020.6566"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-230144","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:54Z","timestamp":1777455654000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-230144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":23,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-230144","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,1]]}}}