期刊
MATERIALS CHARACTERIZATION
卷 49, 期 3, 页码 203-209出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/S1044-5803(02)00323-6
关键词
titanium alloy; grain size; volume fraction; fuzzy neural network
The effect of process parameters on microstructural evolution, including gain size and volume fraction of the alpha-phase during hot forming of a TC6 alloy, was investigated using compression tests. Experiments were conducted on material with (alpha + beta) phases at deformation temperatures of 800, 860, 920 and 950 T, strain rates of 0.001, 0.01, 1 and 50 s(-1), and height reductions of 30%, 40% and 50%. All of the optical micrographs and quantitative metallography showed that deformation process parameters affect the microstructure during hot forming of the TC6 alloy, which appears to have a fuzzy characteristic. According to the experimental results of the TC6 alloy, a model for microstructural evolution established, by applying artificial neural network (ANN) and a fuzzy set (INN) to the microstructural evolution in hot forming proposed by Li et al. [J. Mater. Process. Technol. 123 (2002) 377]. The model presented in this paper can be used to predict the microstructural changes at deformation temperatures of 800-950 degreesC and strain rates of 0.001-50 s(-1). The maximum relative error is not more than 10%, which shows that the predicted results are in satisfactory agreement with the experimental results. It is easy and practical to optimize deformation process parameters by coupling with the finite element (FE) simulation technique. (C) 2002 Published by Elsevier Science Inc.
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