Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain
T. Nickchen, G. Engels, J. Lohn, in: Industrializing Additive Manufacturing, Cham, 2020.
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| Published
| English
Author
Nickchen, Tobias;
Engels, Gregor;
Lohn, Johannes
Department
Publishing Year
Book Title
Industrializing Additive Manufacturing
Conference Date
2020-09-01 – 2020-09-03
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Nickchen T, Engels G, Lohn J. Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain. In: Industrializing Additive Manufacturing. Cham; 2020. doi:10.1007/978-3-030-54334-1_4
Nickchen, T., Engels, G., & Lohn, J. (2020). Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain. In Industrializing Additive Manufacturing. Cham. https://doi.org/10.1007/978-3-030-54334-1_4
@inbook{Nickchen_Engels_Lohn_2020, place={Cham}, title={Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain}, DOI={10.1007/978-3-030-54334-1_4}, booktitle={Industrializing Additive Manufacturing}, author={Nickchen, Tobias and Engels, Gregor and Lohn, Johannes}, year={2020} }
Nickchen, Tobias, Gregor Engels, and Johannes Lohn. “Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain.” In Industrializing Additive Manufacturing. Cham, 2020. https://doi.org/10.1007/978-3-030-54334-1_4.
T. Nickchen, G. Engels, and J. Lohn, “Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain,” in Industrializing Additive Manufacturing, Cham, 2020.
Nickchen, Tobias, et al. “Opportunities of 3D Machine Learning for Manufacturability Analysis and Component Recognition in the Additive Manufacturing Process Chain.” Industrializing Additive Manufacturing, 2020, doi:10.1007/978-3-030-54334-1_4.
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