MATEC Web Conf.
Volume 321, 2020The 14th World Conference on Titanium (Ti 2019)
|Number of page(s)||8|
|Section||Additive and Near Net Shape Manufacturing|
|Published online||12 October 2020|
Machine Learning for Competitive Grain Growth Behavior in Additive Manufacturing Ti6Al4V
1 Department of Mechanical Engineering, McGill University, Montreal, Quebec, Canada H2A0C3
2 State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China
3 Department of Mining and Materials Engineering, McGill University, Montreal, Quebec, Canada H3A0C5
Metal additive manufacturing (MAM) technology is now changing the pattern of the high-end manufacturing industry, among which MAM fabricated Ti6Al4V has been far the most extensively investigated material and attracts a lot of research interests. This work established a deep neural network (DNN) to investigate the grain boundary in competitive grain growth for a bi-crystal system, the column β grains of Ti6Al4V as an example. Because of the limited number of experimental samples, the DNN is trained based on the data coming from the Geometrical Limited criterion. A series of direct energy deposition experiment using Ti6Al4V is carried out under the Taguchi experimental design. The grain boundary angles between the column grains are measured in the experiment and used to evaluate the accuracy of DNN.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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