Generative Adversarial Networks Enable Outlier Detection and Property Monitoring for Additive Manufacturing of Complex Structures [dataset]

Published: 20 August 2024| Version 3 | DOI: 10.17632/zbbrbzrdtd.3
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Description

Microstructure dataset utilized in "Generative Adversarial Networks Enable Outlier Detection and Property Monitoring for Additive Manufacturing of Complex Structures, Engineering Applications of Artificial Intelligence, DOI: 10.1016/j.engappai.2024.108993, October 2024": - lattice structures in folders lattice_structures and lattice_structures_defective were employed in Section 3.1 - spherical voids in folders spherical_voids and spherical_voids_perturbed were employed in Section 3.2 The lattice structures are extracted from the CT scans in "Image-based numerical characterization and experimental validation of tensile behavior of octet-truss lattice structures" (https://doi.org/10.1016/j.addma.2021.101949)

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Categories

Computational Mechanics, Structural Health Monitoring, Deep Learning, Homogenization, Design for Additive Manufacture, Neural Network, Generative Adversarial Network

Funding

ETH Zurich

Postdoctoral Fellowship

Deutsche Forschungsgemeinschaft

414265976 TRR 277 C-01

Deutsche Forschungsgemeinschaft

512730472

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