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Machine Learning-based Microstructure Prediction During Laser Sintering of Alumina by Tang, Jianan (author);Geng, Xiao (author);Li, Dongsheng (author);Shi, Yunfeng (author);Tong, Jianhua (author);Xiao, Hai (author);Peng, Fei (author) is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Machine Learning-based Microstructure Prediction During Laser Sintering of Alumina about?
Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the w
Who reads Machine Learning-based Microstructure Prediction During Laser Sintering of Alumina?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Tang, Jianan (author);Geng, Xiao (author);Li, Dongsheng (author);Shi, Yunfeng (author);Tong, Jianhua (author);Xiao, Hai (author);Peng, Fei (author)
- Publisher
- Springer Science and Business Media LLC
- Published
- 2021
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)