Preprocessing method for robust topography reconstruction of surfaces of metal additive manufactured parts based on focus variation microscopy
Author:
Affiliation:
1. Universität Kassel FB16 Elektrotechnik/Informatik , Fachgebiet Messtechnik , Kassel , Germany
2. Universität Kassel FB15 Maschinenbau, Institut für Werkstofftechnik , Fachgebiet Metallische Werkstoffe , Kassel , Germany
Abstract
Publisher
Walter de Gruyter GmbH
Link
https://www.degruyter.com/document/doi/10.1515/teme-2023-0157/pdf
Reference37 articles.
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2. M. So, G. Seo, D. Kim, and J. Shin, “Prediction of metal additively manufactured surface roughness using deep neural network,” Sensors, vol. 22, no. 20, p. 7955, 2022. https://doi.org/10.3390/s22207955.
3. Y. Kok, et al.., “Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: a critical review,” Mater. Des., vol. 139, pp. 565–586, 2018. https://doi.org/10.1016/j.matdes.2017.11.021.
4. M. Wadge, et al.., “Tailoring absorptivity of highly reflective Ag powders by pulsed-direct current magnetron sputtering for additive manufacturing processes,” J. Mater. Process. Technol., vol. 317, p. 117985, 2023. https://doi.org/10.1016/j.jmatprotec.2023.117985.
5. C. Gomez, R. Su, A. Thompson, J. DiSciacca, S. Lawes, and R. Leach, “Optimization of surface measurement for metal additive manufacturing using coherence scanning interferometry,” Opt. Eng., vol. 56, no. 11, p. 111714, 2017. https://doi.org/10.1117/1.oe.56.11.111714.
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