Robust in-line qualification of lattice structures manufactured via laser powder bed fusion

Robust in-line qualification of lattice structures manufactured via laser powder bed fusion

Bianca Maria Colosimo, Marco Grasso, Federica Garghetti, Luca Pagani

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Abstract. The shape complexity enabled by AM would impose new part inspection systems (e.g., x-ray computed tomography), which translate into qualification time and costs that may be not affordable. However, the layerwise nature of the process potentially allows anticipating qualification tasks in-line and in-process, leading to a quick detection of defects since their onset stage. This opportunity is particularly attractive in the presence of lattice structures, whose industrial adoption has considerably increased thanks to AM. This paper presents a novel methodology to model the quality of lattice structures at unit cell level while the part is being built, using high resolutions images of the powder bed for in-line geometry reconstruction and identification of deviations from the nominal shape. The methodology is designed to translate complex 3D shapes into 1D deviation profiles that capture the “geometrical signature” of the cell together with the reconstruction uncertainty.

Additive Manufacturing, Quality Modelling, Profile Monitoring, Lattice, In-Situ Sensing

Published online 9/5/2023, 9 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Bianca Maria Colosimo, Marco Grasso, Federica Garghetti, Luca Pagani, Robust in-line qualification of lattice structures manufactured via laser powder bed fusion, Materials Research Proceedings, Vol. 35, pp 232-240, 2023


The article was published as article 28 of the book Italian Manufacturing Association Conference

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] Helou, M., & Kara, S. (2018). Design, analysis and manufacturing of lattice structures: an overview. International Journal of Computer Integrated Manufacturing, 31(3), 243-261.
[2] Liu, L., Kamm, P., García-Moreno, F., Banhart, J., & Pasini, D. (2017). Elastic and failure response of imperfect three-dimensional metallic lattices: the role of geometric defects induced by Selective Laser Melting. Journal of the Mechanics and Physics of Solids, 107, 160-184.
[3] Melancon, D., Bagheri, Z. S., Johnston, R. B., Liu, L., Tanzer, M., & Pasini, D. (2017). Mechanical characterization of structurally porous biomaterials built via additive manufacturing: experiments, predictive models, and design maps for load-bearing bone replacement implants. Acta biomaterialia, 63, 350-368.
[4] Dallago, M., Raghavendra, S., Luchin, V., Zappini, G., Pasini, D., & Benedetti, M. (2019). Geometric assessment of lattice materials built via Selective Laser Melting. Materials Today: Proceedings, 7, 353-361.
[5] Colosimo B.M., Grasso, M., Garghetti, F., Rossi, B. (2021), Complex geometries in additive manufacturing: A new solution for lattice structure modeling and monitoring, Journal of Quality Technology.
[6] Liu, C., Le Roux, L., Ji, Z., Kerfriden, P., Lacan, F., & Bigot, S. (2020). Machine Learning-enabled feedback loops for metal powder bed fusion additive manufacturing. Procedia Computer Science, 176, 2586-2595.
[7] Vasileska, E., Demir, A. G., Colosimo, B. M., & Previtali, B. (2020). Layer-wise control of selective laser melting by means of inline melt pool area measurements. Journal of Laser Applications, 32(2), 022057.
[8] Colosimo, B. M., Grossi, E., Caltanissetta, F., & Grasso, M. (2020). Penelope: a novel prototype for in situ defect removal in LPBF. Jom, 72, 1332-1339.
[9] Colosimo, B. M., Garghetti, F., Pagani, L., & Grasso, M. (2022). A novel method for in-process inspection of lattice structures via in-situ layerwise imaging. Manufacturing Letters, 32, 67-72.
[10] Guerra, M. G., Lafirenza, M., Errico, V., & Angelastro, A. (2023). In-process dimensional and geometrical characterization of laser-powder bed fusion lattice structures through high-resolution optical tomography. Optics & Laser Technology, 162, 109252.
[11] Dewulf, W., Bosse, H., Carmignato, S., & Leach, R. (2022). Advances in the metrological traceability and performance of X-ray computed tomography. CIRP Annals, 71(2), 693-716.
[12] Withers, P. J., Bouman, C., Carmignato, S., Cnudde, V., Grimaldi, D., Hagen, C. K., … & Stock, S. R. (2021). X-ray computed tomography. Nature Reviews Methods Primers, 1(1), 18.
[13] Szeliski, R. (2022). Image alignment and stitching. In Computer Vision (pp. 401-441). Springer, Cham.
[14] Avants, B.B., Tustison, N.J., Stauffer, M., Song, G., Wu, B., Gee, J.C. (2014) The insight toolkit image registration framework, Front. Neuroinform. 8(44).
[15] Liu, S., & Peng, Y. (2012). A local region-based Chan-Vese model for image segmentation. Pattern Recognition, 45(7), 2769-2779.
[16] Soomro, S., Munir, A., & Choi, K. N. (2018). Hybrid two-stage active contour method with region and edge information for intensity inhomogeneous image segmentation. PloS one, 13(1), e0191827.
[17] Caltanissetta, F., Grasso, M., Petro, S., & Colosimo, B. M. (2018). Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion. Additive Manufacturing, 24, 183-199.
[18] Aminzadeh, M., & Kurfess, T. (2016, June). Vision-based inspection system for dimensional accuracy in powder-bed additive manufacturing. In International manufacturing science and engineering conference (Vol. 49903, p. V002T04A042). American Society of Mechanical Engineers.
[19] Pagani, L., Grasso, M., Scott, P. J., & Colosimo, B. M. (2020). Automated layerwise detection of geometrical distortions in laser powder bed fusion. Additive Manufacturing, 36, 101435.