Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld Strength
Friction stir welding (FSW) is today used as a premier solution for joining non-ferrous metals, although there are many limitations in its application. One of the objectives of this study was to propose an innovative welding technique, namely ultrasonic-assisted friction stir welding (UAFSW) with lo...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/7/1101 |
| _version_ | 1797417043179339776 |
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| author | Fei Xue Diqiu He Haibo Zhou |
| author_facet | Fei Xue Diqiu He Haibo Zhou |
| author_sort | Fei Xue |
| collection | DOAJ |
| description | Friction stir welding (FSW) is today used as a premier solution for joining non-ferrous metals, although there are many limitations in its application. One of the objectives of this study was to propose an innovative welding technique, namely ultrasonic-assisted friction stir welding (UAFSW) with longitudinal ultrasonic vibration applied to the stirring head. In this paper, UAFSW mechanical properties and microstructure analysis were performed to demonstrate that the fluidity of the weld area was improved and the strengthened phase organization was partially preserved, due to the application of ultrasonic vibration. The addition of 1.8 kW of ultrasonic vibration at 1200 rpm and 150 mm/min welding parameters resulted in a 10.5% increase in the tensile strength of the weld. The ultimate tensile strength of 2219 aluminum alloy UAFSW was analyzed and predicted using mathematical modeling and machine learning techniques. A full factorial design method with multiple regression, random forest, and support vector machine was used to validate the experimental results. In predicting the tensile behavior of UAFSW joints, by comparing the evaluation metrics, such as R<sup>2</sup>, MSE, RMSE, and MAE, it was found that the RF model was 22% and 21% more accurate in the R<sup>2</sup> metric compared to other models, and RF was considered as the best performing machine learning method. |
| first_indexed | 2024-03-09T06:13:10Z |
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| id | doaj.art-e909181ab4fa4e61a2cd8c14cb475909 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T06:13:10Z |
| publishDate | 2022-06-01 |
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| record_format | Article |
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| spelling | doaj.art-e909181ab4fa4e61a2cd8c14cb4759092023-12-03T11:56:11ZengMDPI AGMetals2075-47012022-06-01127110110.3390/met12071101Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld StrengthFei Xue0Diqiu He1Haibo Zhou2State Key Laboratory of High-Performance Complex Manufacturing, Central South University, Changsha 410083, ChinaState Key Laboratory of High-Performance Complex Manufacturing, Central South University, Changsha 410083, ChinaState Key Laboratory of High-Performance Complex Manufacturing, Central South University, Changsha 410083, ChinaFriction stir welding (FSW) is today used as a premier solution for joining non-ferrous metals, although there are many limitations in its application. One of the objectives of this study was to propose an innovative welding technique, namely ultrasonic-assisted friction stir welding (UAFSW) with longitudinal ultrasonic vibration applied to the stirring head. In this paper, UAFSW mechanical properties and microstructure analysis were performed to demonstrate that the fluidity of the weld area was improved and the strengthened phase organization was partially preserved, due to the application of ultrasonic vibration. The addition of 1.8 kW of ultrasonic vibration at 1200 rpm and 150 mm/min welding parameters resulted in a 10.5% increase in the tensile strength of the weld. The ultimate tensile strength of 2219 aluminum alloy UAFSW was analyzed and predicted using mathematical modeling and machine learning techniques. A full factorial design method with multiple regression, random forest, and support vector machine was used to validate the experimental results. In predicting the tensile behavior of UAFSW joints, by comparing the evaluation metrics, such as R<sup>2</sup>, MSE, RMSE, and MAE, it was found that the RF model was 22% and 21% more accurate in the R<sup>2</sup> metric compared to other models, and RF was considered as the best performing machine learning method.https://www.mdpi.com/2075-4701/12/7/1101ultrasonic-assisted friction stir weldingprocess parametermachine learningmicrostructure |
| spellingShingle | Fei Xue Diqiu He Haibo Zhou Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld Strength Metals ultrasonic-assisted friction stir welding process parameter machine learning microstructure |
| title | Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld Strength |
| title_full | Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld Strength |
| title_fullStr | Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld Strength |
| title_full_unstemmed | Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld Strength |
| title_short | Effect of Ultrasonic Vibration in Friction Stir Welding of 2219 Aluminum Alloy: An Effective Model for Predicting Weld Strength |
| title_sort | effect of ultrasonic vibration in friction stir welding of 2219 aluminum alloy an effective model for predicting weld strength |
| topic | ultrasonic-assisted friction stir welding process parameter machine learning microstructure |
| url | https://www.mdpi.com/2075-4701/12/7/1101 |
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