Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes
The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. The research addresses the challenges...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/20/9309 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205690399817728 |
|---|---|
| author | Mingoo Cho Jinsu Gim Ji Hoon Kim Sungwook Kang |
| author_facet | Mingoo Cho Jinsu Gim Ji Hoon Kim Sungwook Kang |
| author_sort | Mingoo Cho |
| collection | DOAJ |
| description | The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. The research addresses the challenges posed by differences in material properties and the complex nature of FSW, where traditional experimental methods are time-consuming and costly. FSW experiments were conducted under a variety of conditions, and the resulting temperature data were utilized as input for a heat transfer analysis. The maximum temperature and temperature gradient obtained from the analysis were employed as input variables for training the ANN. The ANN was optimized using the Hyperband tuner and validated against experimental results. The model successfully predicted tensile strength with an average error of 5.4%, demonstrating its potential for predicting mechanical properties under different welding conditions. This approach offers a more efficient and accurate method for optimizing FSW processes. |
| format | Article |
| id | doaj-art-faa2fd1a66b34db8b2fbf30da2d5f280 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-faa2fd1a66b34db8b2fbf30da2d5f2802025-08-20T02:11:01ZengMDPI AGApplied Sciences2076-34172024-10-011420930910.3390/app14209309Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic ProcessesMingoo Cho0Jinsu Gim1Ji Hoon Kim2Sungwook Kang3Department of Mechanical Engineering, Pusan National University, Busan 46241, Republic of KoreaExtreme Process Control R&D Group, Korea Institute of Industrial Technology, Jinju 52845, Republic of KoreaDepartment of Mechanical Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Smart Ocean Mobility Engineering, Changwon National University, Changwon 51140, Republic of KoreaThe objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. The research addresses the challenges posed by differences in material properties and the complex nature of FSW, where traditional experimental methods are time-consuming and costly. FSW experiments were conducted under a variety of conditions, and the resulting temperature data were utilized as input for a heat transfer analysis. The maximum temperature and temperature gradient obtained from the analysis were employed as input variables for training the ANN. The ANN was optimized using the Hyperband tuner and validated against experimental results. The model successfully predicted tensile strength with an average error of 5.4%, demonstrating its potential for predicting mechanical properties under different welding conditions. This approach offers a more efficient and accurate method for optimizing FSW processes.https://www.mdpi.com/2076-3417/14/20/9309friction stir weldingdissimilar materialsaluminum alloycopper alloyfinite element methodtensile strength |
| spellingShingle | Mingoo Cho Jinsu Gim Ji Hoon Kim Sungwook Kang Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes Applied Sciences friction stir welding dissimilar materials aluminum alloy copper alloy finite element method tensile strength |
| title | Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes |
| title_full | Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes |
| title_fullStr | Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes |
| title_full_unstemmed | Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes |
| title_short | Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes |
| title_sort | development of an artificial neural network model to predict the tensile strength of friction stir welding of dissimilar materials using cryogenic processes |
| topic | friction stir welding dissimilar materials aluminum alloy copper alloy finite element method tensile strength |
| url | https://www.mdpi.com/2076-3417/14/20/9309 |
| work_keys_str_mv | AT mingoocho developmentofanartificialneuralnetworkmodeltopredictthetensilestrengthoffrictionstirweldingofdissimilarmaterialsusingcryogenicprocesses AT jinsugim developmentofanartificialneuralnetworkmodeltopredictthetensilestrengthoffrictionstirweldingofdissimilarmaterialsusingcryogenicprocesses AT jihoonkim developmentofanartificialneuralnetworkmodeltopredictthetensilestrengthoffrictionstirweldingofdissimilarmaterialsusingcryogenicprocesses AT sungwookkang developmentofanartificialneuralnetworkmodeltopredictthetensilestrengthoffrictionstirweldingofdissimilarmaterialsusingcryogenicprocesses |