SVR-Based Predictive Model for 2519 Aluminum Alloy

J.L. Tang; H.Y. Liu; M.H. Gui; J.Y. Tang

doi:10.4028/www.scientific.net/KEM.719.41

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HomeKey Engineering MaterialsKey Engineering Materials Vol. 719SVR-Based Predictive Model for 2519 Aluminum Alloy

SVR-Based Predictive Model for 2519 Aluminum Alloy

Abstract:

For 2519 aluminum alloy, there are very complex nonlinear relations among the thermal dynamical parameters in the process of deforming. In this paper, the support vector regression (SVR) approach is proposed to establish a model for predicting flow stress of 2519 alloy base on the flows tress experimental data of 2519 aluminum alloy under two influential factors, including strain and strain rate. Research showed that the prediction precision of SVR model is high enough: the mean absolute error (MAE) is 0.181, mean absolute percentage error (MAPE) is 0.434%, root mean square error (RMSE) is 0.22, multiple correlation coefficient (R²) is 0.998. This research suggests that SVR is an effective and powerful tool for predicting the flow stress of 2519 aluminum alloy.

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Periodical:

Key Engineering Materials (Volume 719)

Pages:

41-45

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.719.41

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Online since:

November 2016

Authors:

J.L. Tang, H.Y. Liu, M.H. Gui, J.Y. Tang

Keywords:

Aluminium Alloy, Modeling, Particle Swarm Optimization (PSO), Regression Analysis, Support Vector Regression (SVR)

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