Investigation of constitutive model of as-extruded spray-forming 7055 aluminum alloy based on BP artificial neural network

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Investigation of constitutive model of as-extruded spray-forming 7055 aluminum alloy based on BP artificial neural network

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LUO Rui / CAO Yun / QIU Yu / CUI Shugang / ZHOU Haotian / ZHOU Yiming / YUAN Fei / ZHANG Xiaopeipei / CHENG Xiaonong

Gleeble physical simulation technique was employed to investigate the high-temperature flow stress characteristics of the studied spray forming 7055 aluminum alloy. Simultaneously, the Arrhenius constitutive model which couples the parameter of true strain and the BP artificial neural network constitutive model were contrastingly utilized to predict the flow stress behavior of the experimental alloy. The result shows that the flow stress of spray forming 7075 aluminum alloy is significantly affected by deformation parameter, which is negative correlated with deformation temperature and positively correlated with strain rate. Through the comparison of the two models, the average relative error of the Arrhenius constitutive model lies over 2%. And the error of the model tends to increase with the rising temperature. Moreover, the average absolute error and the average relative error reach the maximum at hot processing temperature (around 450 ℃). It is difficult to precisely predict the flow stress characteristics of the alloy. However, BP artificial neural network constitutive model has higher prediction accuracy, the average relative error δ value is only 0.813% and has higher temperature stability.

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Title :

Investigation of constitutive model of as-extruded spray-forming 7055 aluminum alloy based on BP artificial neural network

Contributors:

LUO Rui (author) / CAO Yun (author) / QIU Yu (author) / CUI Shugang (author) / ZHOU Haotian (author) / ZHOU Yiming (author) / YUAN Fei (author) / ZHANG Xiaopeipei (author) / CHENG Xiaonong (author)

Published in:

Journal of Aeronautical Materials, Vol 41, Iss 1, Pp 35-44 (2021)

Publication date :

2021

ISSN :

1005-5053

DOI :

https://doi.org/10.11868/j.issn.1005-5053.2020.000089

Type of media :

Article (Journal)

Type of material :

Electronic Resource