Title:

Regression modeling and neural computing for predicting the Ultimate Tensile Strength of Friction Stir Welded aerospace aluminium alloy

Creator:

Mishra , Akshansh ; Ve Vance, Jonathan

Subject:

artificial neural network ; regression model ; friction stir welding

Description:

AA7075 is an aluminum alloy that's 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 couldn't be securely welded together by the traditional welding process. Friction Stir Welding (FSW) process overcomes the limitations of the conventional welding process. The aim of our present 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.

Place of publishing:

Koszalin

Publisher:

Publishing House of the Koszalin University of Technology

Date:

2019

Type:

artykuł w czasopiśmie

Format:

application/pdf

Language:

eng

Is part of:

Journal of Mechanical and Energy Engineering. Vol.3 (43), nr 3, s. 221-226

Rights:

Biblioteka Politechniki Koszalińskiej

Access rights:

internet

License:

Creative Commons BY 4.0