Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 stainless steel
Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński · 2024
Read Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 stainless steel by Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11667624.txt.
Description
Marwan Mezher and co-authors work on welding two unlike metals in a 2024 study: a titanium alloy and a stainless steel. Joints of this kind are prized in industry, and they are made by resistance spot welding, which fuses metal with heat from an electric current. Picking the right settings is hard because the two metals behave so differently.
The team ran welding tests with sheets of equal and unequal thickness. They then trained machine learning and neural network models on the test results to predict joint quality. The models help find the settings that give the strongest joint without wasted trials. Better settings mean fewer weak welds in parts that must join light titanium to tough steel.
Catalog record
| CREATOR | Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 86 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |