Class
Article
College
College of Science
Department
Mathematics and Statistics Department
Faculty Mentor
Zilong Song
Presentation Type
Oral Presentation
Abstract
Joining aluminum alloys and other metal workpieces is a typical manufacturing process, which can be accomplished by various welding techniques. Friction stir welding (FSW), a relatively new technology patented in 1991, has many advantages over conventional welding processes. For the FSW process, it is desirable to determine an optimal set of parameters to avoid product defects in the joints. Modeling and simulating the FSW process is computationally expensive which creates a bottleneck in searching for optimal operating parameters. Reduced order modeling techniques will be used to significantly reduce computation time for FSW models while maintaining accuracy. In addition, machine learning techniques will be used to further improve the computational efficiency and make predictions over various operating parameters in the FSW process.
Location
Logan, UT
Start Date
4-11-2023 1:30 AM
End Date
4-11-2023 2:30 AM
Included in
Fast Computation of Friction Stir Welding Process With Model Order Reduction and Machine Learning
Logan, UT
Joining aluminum alloys and other metal workpieces is a typical manufacturing process, which can be accomplished by various welding techniques. Friction stir welding (FSW), a relatively new technology patented in 1991, has many advantages over conventional welding processes. For the FSW process, it is desirable to determine an optimal set of parameters to avoid product defects in the joints. Modeling and simulating the FSW process is computationally expensive which creates a bottleneck in searching for optimal operating parameters. Reduced order modeling techniques will be used to significantly reduce computation time for FSW models while maintaining accuracy. In addition, machine learning techniques will be used to further improve the computational efficiency and make predictions over various operating parameters in the FSW process.