@inproceedings{ab0da256a3474ba6a153a90daf959074,
title = "Modeling the correlation between microstructure and tensile properties of Ti-17 alloy using artificial neural network",
abstract = "In this work, a relational model was established correlating microstructure and tensile properties for the Ti-17 alloy using a back-propagation (BP) neural network technique. In the proposed model, the input data consisted of quantitative microstructural feature parameters, including the volume fraction, thickness and Ferret ratio of α phase. Meanwhile, the tensile properties are the outputs of the model, such as ultimate tensile strength, yield strength, elongation and reduction in area. The coefficient of determination is more than 0.900, which indicates that the developed model possesses the excellent ability to predict the internal relationship of the microstructure and tensile properties of Ti-17 alloy.",
keywords = "BP neural network, Microstructure, Tensile properties, Ti-17 alloy",
author = "Jia, {Zhi Qiang} and Zeng, {Wei Dong}",
year = "2014",
doi = "10.4028/www.scientific.net/AMR.983.127",
language = "英语",
isbn = "9783038351573",
series = "Advanced Materials Research",
publisher = "Trans Tech Publications Ltd",
pages = "127--130",
booktitle = "Advanced Materials and Engineering",
note = "Annual International Conference on Intelligent Materials and Nanomaterials, AIMN 2014 ; Conference date: 18-04-2014 Through 19-04-2014",
}