Abstract
An improved neural network model was developed for prediction of mechanical properties in the design and development of new types of magnesium alloys by refining the types of input variables and using a more reasonable algorithm. The results showed that the improved model apparently decreased the prediction errors, and raised the accuracy of the prediction results. Better preprocessing parameters were found to be [0.15, 0.90] for the tensile strength, [0.1, 0.9] for the yield strength, and [0.15, 0.90] for the elongation. When the above parameters were used, the relativity for predicition of strength was bigger than 0.95. By using improved ANN analysis, more reasonable process parameters and composition could be obtained in some magnesium alloys without addition of strontoum.
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References
Pan F S, Yang M B, Ma Y L, et al. Research and development of processing technologies for wrought magnesium alloys. Mater Sci Forum, 2007, 546–549(1): 37–48
Yang M B, Pan F S, Cheng R,et al. Comparison about efficiency of Al-10Sr and Mg-10Sr master alloys to grain refinement of AZ31 magnesium alloy. J Mater Sci, 2007, 42(24): 10074–10079
Pan F S, Yang M B, Zhang D F. Research and development of wrought magnesium alloys in China. Mater Sci Forum, 2005, 488–489(2): 413–418
Luo A A. Recent magnesium alloy development for elevated temperature applications. Int Mater Rev, 2004, 49(1): 13–30
Pekguleryuz M O, Baril E. J. Creep resistant magnesium diecasting alloys based on Mg-Al- (alkaline earth element) systems. Mater Trans, 2001, 42(7): 1258–1267
Tang A T, Pan F S, Yang M B, et al. Mechanical properties and microstructure of magnesium-aluminum based alloys containing strontium. Mater Trans, 2008, 49(6): 1203–1211
Mordike B L, Ebert T. Magnesium properties-applications-potential. Mater Sci Eng A-struct, 2001, 302(1): 37–45.
Malinova T, Guo Z X. Artificial neural network modelling of hydrogen storage properties of Mg-based alloys. Mater Sci Eng A—struct, 2004, 365(1–2): 219–227
Sungmoon J, Jamshid G. Neural network constitutive for rate-dependent materials. Comput Struct, 2006, 84(15–16): 955–963
Hsiang S H, Kuo J L. Applying ANN to predict the forming load and mechanical property of magnesium alloy under hot extrusion. Int J Adv Manuf Tech, 2005, 26(9–10): 970–977
Hsiang S H, Kuo J L, Yang F Y. Using artificial neural networks to investigate the influence of temperature on hot extrusion of AZ61 magnesium alloy. J Intell Manuf, 2006, 17(2): 191–201
Liu H D, Tang A T, Pan F S, et al. A model on the correlation between composition and mechanical properties of Mg-Al-Zn alloys by using artificial neural network. Mater Sci Forum, 2005, 488–489(2): 793–796
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Supported by the National Natural Science Foundation of China (Grant No. 50725413), the National Basic Research Program of China (“973” Project) (Grant No. 2007CB613704), and the National Key Technologies R&D Program of China (Grant No. 2006BAE04B09-7)
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Tang, A., Liu, B., Pan, F. et al. An improved neural network model for prediction of mechanical properties of magnesium alloys. Sci. China Ser. E-Technol. Sci. 52, 155–160 (2009). https://doi.org/10.1007/s11431-008-0278-3
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DOI: https://doi.org/10.1007/s11431-008-0278-3