Experimental, modelling and simulation of an approach for optimizing the superplastic forming of Ti-6%Al-4%V titanium alloy
Mosleh, A.O and Mikhaylovskaya, A.V and Kotov, A.D and Kwame, J.S. (2019) Experimental, modelling and simulation of an approach for optimizing the superplastic forming of Ti-6%Al-4%V titanium alloy. Journal of Manufacturing Processes, 45. pp. 262-272. ISSN 1526-6125
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Abstract
The study presents an integrated approach for superplastic forming of Ti-6%Al-4%V titanium alloy. The flow behavior of the studied alloy was investigated using uniaxial constant strain rate tensile tests in a temperature range of 800–900 °C and a strain rate range of 3 × 10−4–3 × 10-3s-1. The obtained flow behavior was modeled using the simple Johnson-Cook (S J-C), modified Johnson-Cook (M J-C) and artificial neural network (ANN) models. An assessment study between the constructed models was performed in order to evaluate the predictability of each model. Standard statistical comparative quantities such as correlation coefficient (R), mean absolute relative error (AARE) and the root mean square error (RMSE) were used to ascertain the model viability. The S J-C model proved ineffectual in predicting the flow behavior of Ti-6%Al-4%V alloy. The M J-C and ANN models are able to successfully describe the flow behavior of the alloy. The validity of the model used for the simulation was ascertained by testing the predicted data with the constructed models at a temperature of 875 °C and a strain rate of 2 × 10-3s-1 using DEFORM 3D finite element simulation (FES). The obtained results from the FES were verified with the experimental results after superplastic forming process. The FES results show the possibility of using uniaxial tensile test data to simulate superplastic forming process of the Ti-6%Al-4%V titanium sheets.
Creators(s): |
Mosleh, A.O, Mikhaylovskaya, A.V, Kotov, A.D and Kwame, J.S. ![]() | Item type: | Article |
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ID code: | 69009 |
Keywords: | titanium alloys, constitutive modeling, Johnson-Cook (J-C) models, artificial neural network, superplastic forming, finite elements analysis, Engineering design, Materials Science(all) |
Subjects: | Technology > Engineering (General). Civil engineering (General) > Engineering design |
Department: | Faculty of Engineering > Design, Manufacture and Engineering Management |
Depositing user: | Pure Administrator |
Date deposited: | 24 Jul 2019 12:23 |
Last modified: | 05 Feb 2021 04:09 |
URI: | https://strathprints.strath.ac.uk/id/eprint/69009 |
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