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
Contributors:
Alexander Henkes, Leon Herrmann, Henning Wessels, Stefan KollmannsbergerDescription
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