- Tytuł:
- Regression modeling and neural computing for predicting the ultimate tensile strength of friction stir welded aerospace aluminium alloy
- Autorzy:
-
Mishra, Akshansh
Vance, Jonathan Ve - Powiązania:
- https://bibliotekanauki.pl/articles/95289.pdf
- Data publikacji:
- 2019
- Wydawca:
- Politechnika Koszalińska. Wydawnictwo Uczelniane
- Tematy:
-
artificial neural networks
regression model
friction stir welding
sztuczne sieci neuronowe
model regresji
zgrzewanie tarciowe - Opis:
- AA7075 is an aluminum alloy which is almost as strong as steel, yet it weighs just one third as much. Unfortunately its use has been limited, due to the fact that pieces of it could not be securely welded together by the traditional welding process. Friction Stir Welding (FSW) process overcomes the limitations of conventional welding process. In our present work we have used Artificial Neural Network which is Artificial Intelligence based technique used for prediction purpose. The main objective of our present work is to compare the predicted results of the Ultimate Tensile Strength (UTS) of Friction Stir welded similar joints through Regression modeling and Artificial Neural Network (ANN) modeling. It was observed that the linear regression algorithm is able to make more accurate predictions compared to neural network algorithm for small dataset.
- Źródło:
-
Journal of Mechanical and Energy Engineering; 2019, 3, 3; 221-226
2544-0780
2544-1671 - Pojawia się w:
- Journal of Mechanical and Energy Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł