About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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Additive Manufacturing of Ceramic-based Materials: Process Development, Materials, Process Optimization and Applications
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Presentation Title |
Exploration of the Underlying Space in Microscopic Images via Deep Learning for Additively Manufactured Piezoceramics |
Author(s) |
Wenhua Yang, Zhuo Wang, Tiannan Yang, Li He, Xuan Song, Yucheng Liu, Lei Chen |
On-Site Speaker (Planned) |
Lei Chen |
Abstract Scope |
Existing DL-based methods are generally limited in generating (1) microstructures with high resolution, (2) microstructures with high variability, (3) microstructures with guaranteed periodicity, and (4) highly controllable microstructures. In this study, a DL approach based on a stacked generative adversarial network (StackGAN-v2) is proposed to overcome these shortcomings. The presented modeling approach can reconstruct high-fidelity microstructures of additively manufactured piezoceramic, which are statistically equivalent to original microstructures either experimentally observed or numerically predicted. Advantages of the proposed modeling approach are also illustrated in terms of its capability in controlling the probability density function (PDF) of grain size, grain orientation, and micropore in a large space, which would have significant benefits in exploring the effects of these microstructure features on the piezoelectricity of piezoceramics. Therefore, this DL approach can significantly accelerate the process of designing optimal microstructures when integrating with computational methods to achieve desired piezoelectric properties. |