Zobrazeno 1 - 10
of 1 865
pro vyhledávání: '"Wing Kam"'
Autor:
Abdullah Al Amin, Yangfan Li, Ye Lu, Xiaoyu Xie, Zhengtao Gan, Satyajit Mojumder, Gregory J. Wagner, Wing Kam Liu
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-14 (2024)
Abstract Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track lase
Externí odkaz:
https://doaj.org/article/edb26e75c946464da7b892698808e29f
Simulation-free determination of microstructure representative volume element size via Fisher scores
Publikováno v:
APL Machine Learning, Vol 2, Iss 2, Pp 026101-026101-13 (2024)
A representative volume element (RVE) is a reasonably small unit of microstructure that can be simulated to obtain the same effective properties as the entire microstructure sample. Finite element (FE) simulation of RVEs, as opposed to much larger sa
Externí odkaz:
https://doaj.org/article/753b00d54aae49a6a13a411e1b365568
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
Dimension reduction techniques allow to simplify complex process design and system optimization in various engineering problems. The authors propose here a machine learning approach to discover dominant dimensionless numbers and governing laws from s
Externí odkaz:
https://doaj.org/article/c8217a5b2e0c47009abe9933b6c2f724
Publikováno v:
APL Machine Learning, Vol 1, Iss 3, Pp 036112-036112-14 (2023)
The localized stress and strain field simulation results are critical for understanding the mechanical properties of materials, such as strength and toughness. However, applying off-the-shelf machine learning or deep learning methods to a digitized m
Externí odkaz:
https://doaj.org/article/66a733a564d64f9aa6d261c00534a47c
Publikováno v:
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-28 (2022)
Abstract Background X chromosome inactivation (XCI) is an epigenetic phenomenon that one of two X chromosomes in females is transcriptionally silenced during early embryonic development. Skewed XCI has been reported to be associated with some X-linke
Externí odkaz:
https://doaj.org/article/3aaca3cbf88c42398164d375e6dcd269
Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-12 (2021)
Abstract Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the material
Externí odkaz:
https://doaj.org/article/1a6a60506a7b450a9fccb2e8b2a9a37b
Autor:
Zhengtao Gan, Orion L. Kafka, Niranjan Parab, Cang Zhao, Lichao Fang, Olle Heinonen, Tao Sun, Wing Kam Liu
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-8 (2021)
Identifying scaling laws in metal 3D printing is key to process optimization and materials development. Here the authors report scaling laws to quantify correlation between process parameters, keyhole stability and pore formation by high-speed synchr
Externí odkaz:
https://doaj.org/article/63c1c6b17f2348d59bb5b6fbc0295dc0
Autor:
Guo, Jiachen, Park, Chanwook, Xie, Xiaoyu, Sang, Zhongsheng, Wagner, Gregory J., Liu, Wing Kam
A common trend in simulation-driven engineering applications is the ever-increasing size and complexity of the problem, where classical numerical methods typically suffer from significant computational time and huge memory cost. Methods based on arti
Externí odkaz:
http://arxiv.org/abs/2409.00329
Autor:
Zhengtao Gan, Hengyang Li, Sarah J. Wolff, Jennifer L. Bennett, Gregory Hyatt, Gregory J. Wagner, Jian Cao, Wing Kam Liu
Publikováno v:
Engineering, Vol 5, Iss 4, Pp 730-735 (2019)
To design microstructure and microhardness in the additive manufacturing (AM) of nickel (Ni)-based superalloys, the present work develops a novel data-driven approach that combines physics-based models, experimental measurements, and a data-mining me
Externí odkaz:
https://doaj.org/article/f49e362bec9a48e29368f7c418c558a5
A seamless integration of neural networks with Isogeometric Analysis (IGA) was first introduced in [1] under the name of Hierarchical Deep-learning Neural Network (HiDeNN) and has systematically evolved into Isogeometric Convolution HiDeNN (in short,
Externí odkaz:
http://arxiv.org/abs/2406.03307