Machine Learning for Competitive Grain Growth Behavior in Additive Manufacturing Ti6Al4V

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...

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Bibliographic Details
Published in:MATEC Web of Conferences 2020, Vol.321, p.3004
Main Authors: Li, Jinghao, Sage, Manuel, Zhou, Xianglin, Brochu, Mathieu, Zhao, Yaoyao Fiona
Format: Article
Language:English
Subjects:
Additive manufacturing
Angles (geometry)
Artificial neural networks
Business competition
Design of experiments
Grain boundaries
Grain growth
Machine learning
Titanium base alloys
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Summary: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.
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/202032103004