Journal
MATERIALS SCIENCE AND TECHNOLOGY
Volume 24, Issue 7, Pages 815-821Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1179/174328408X278484
Keywords
Al-4Cu-Mg alloy; semisolid compression; fuzzy neural network; model of microstructural evolution
Funding
- Foundational Research Plan of Jiangsu Province, China [BK2005025]
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The effects of process parameters on microstructural evolution, including grain size and shape factor of the alpha solid particles during semisolid compression of an Al-4Cu-Mg alloy, were investigated. Experiments were conducted at deformation temperatures of 540, 560 and 580 degrees C, strain rates of 0.001, 0.01, 0.1 and 1 s(-1), and height reductions of 20, 40 and 60%. All of the optical micrographs and quantitative metallography showed that deformation process parameters significantly affect the microstructure during semisolid compression of Al-4Cu-Mg alloy, which appears to have a fuzzy characteristic. According to the experimental results from the semisolid compression of Al-4Cu-Mg alloy, a model has been established to describe microstructural evolution by applying a fuzzy set and artificial neural network, which integrates the learning power of neural networks with fuzzy inference systems. The model presented in the present paper can be applied to predict the microstructural changes at deformation temperatures of 540-580 degrees C and strain rates of 0.001-1 s(-1). The maximum relative difference of grain size is 9.34%. The predicted results are in satisfactory agreement with the experimental results.
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