Regression modeling and neural computing for predicting the ultimate tensile strength of friction stir welded aerospace aluminium alloy

• Autorzy: Mishra Akshansh , Vance Jonathan Ve

Identyfikatory

DOI

10.30464/jmee.2019.3.3.221

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial neural networks; regression model; friction stir welding

PL

sztuczne sieci neuronowe; model regresji; zgrzewanie tarciowe

• Wydawca: Wydawnictwo Uczelniane Politechniki Koszalińskiej
• Czasopismo: Journal of Mechanical and Energy Engineering
• Rocznik: 2019
• Tom: Vol. 3, No 3
• Strony: 221--226
• Opis fizyczny: Bibliogr. 4 poz., rys., tab., wykr.

Twórcy

autor

Mishra Akshansh

• Center for Computational Intelligence Friction Stir Welding, Stir Research Technologies, Uttar Pradesh, India

autor

Vance Jonathan Ve

• Department of Engineering Design, Indian Institute of Technology Madras, Ćennaj, Tamilnadu, India

Bibliografia

• 1. Mishra, R.S. and Ma, Z.Y., 2005. Friction stir welding and processing. Materials science and engineering: R: reports, 50(1-2), pp.1-78.
• 2. Mishra, A., 2018. The Use of Artificial Neural Network in Friction Stir Welding Research. Available at SSRN 3301107. https://dx.doi.org/10.2139/ssrn.3301107
• 3. Chiteka, K., 2014. Artificial neural networks in tensile strength and input parameter prediction in friction stir welding.
• 4. Ghetiya, N.D. and Patel, K.M., 2014. Prediction of tensile strength in friction stir welded aluminium alloy using artificial neural network. Procedia Technology, 14, pp.274-281, https://doi.org/10.1016/j.protcy.2014.08.036 S. K. Gupta et al., "Comparison of ANN and Regression Modeling for Predicting the Responses of Friction Stir Welded Dissimilar AA5083-AA6063 Aluminum Alloys Joint", Applied Mechanics and Materials, Vols. 813-814, pp. 415-419, 2015, https://doi.org/10.4028/www.scientific.net/AMM.813814.415