In situ laser profilometry for material segmentation and digital reconstruction of a multicomponent additively manufactured part
Source abstract
Abstract
In addition to its ability to produce geometrically complex parts, additive manufacturing offers a unique opportunity to collect data about a component while it is being fabricated. However, there has only been limited effort to characterize parts morphologically and compositionally in situ. In this article, we present a layer-by-layer, laser profilometry-based in situ characterization technique as a method to digitally reconstruct a multi-material part. Data collected by the laser profilometer yields height maps and grayscale images which are voxelized using purpose-built software to volumetrically reconstruct the part. The same part was also analyzed using X-ray computed tomography (CT) which was not able to resolve the different compositional regions within the part, but captured the filament morphology. The part was then bisected to compare the digital reconstruction to the actual part morphology and composition. Overall, the digital reconstruction was in good agreement with both the CT and bisected images. Deviations between the digital reconstruction and the CT/bisected images are likely the result of image segmentation settings or material shifts after data was collected. The in situ characterization method demonstrated here sets the stage for real time process monitoring and paves the way for additively manufactured parts that are “born qualified.”
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Advanced X-ray and CT Imaging · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Gibson · 2021
2. Recent advances in multi-material additive manufacturing: methods and applications10.1016/j.coche.2020.03.004 ↗
3. 3D printing of materials with tunable failure via bioinspired mechanical gradients10.1002/adma.201705808 ↗
4. Active mixing of reactive materials for 3D printing10.1002/adem.201900147 ↗
5. 3D printing of compositional gradients using the microfluidic circuit analogy10.1002/admt.201900784 ↗
6. Multi-material direct ink writing (DIW) for complex 3D metallic structures with removable supports10.1021/acsami.8b19986 ↗
7. Controlling material reactivity using architecture10.1002/adma.201504286 ↗
8. Recent development in 3D food printing10.1080/10408398.2015.1094732 ↗
9. M. a.S.R. Saadi, A. Maguire, N. Pottackal, M.S.H. Thakur, M.Md. Ikram, A.J. Hart, P.M. Ajayan, M.M. Rahman, Direct Ink Writing: A 3D Printing Technology for Diverse Materials, Adv. Mater. n/a (n.d.) 2108855. https://doi.org/10.1002/adma.202108855.10.1002/adma.202108855 ↗
10. A review of the current progress and application of 3D printed concrete10.1016/j.compositesa.2019.105533 ↗
11. L. Koester, H. Taheri, L.J. Bond, D. Barnard, J. Gray, Additive manufacturing metrology: State of the art and needs assessment, in: Minneapolis, Minnesota, 2016: p. 130001. https://doi.org/10.1063/1.4940604.10.1063/1.4940604 ↗
12. In-situ measurement and monitoring methods for metal powder bed fusion: an updated review10.1088/1361-6501/ac0b6b ↗
13. Computed tomography characterisation of additive manufacturing materials10.1016/j.medengphy.2010.12.015 ↗
14. Comparison of different additive manufacturing methods using computed tomography10.1016/j.csndt.2016.05.008 ↗
15. X-ray microcomputed tomography in additive manufacturing: a review of the current technology and applications10.1089/3dp.2018.0060 ↗
16. Steger · 2018
17. Initial work on the characterization of additive manufacturing (3D printing) using software image analysis10.3390/machines3020055 ↗
18. Open source computer vision-based layer-wise 3D printing analysis
19. MultiFab: a machine vision assisted platform for multi-material 3D printing10.1145/2766962 ↗
20. An application of 3D model reconstruction and augmented reality for real-time monitoring of additive manufacturing10.1016/j.procir.2019.03.060 ↗
21. In situ real time defect detection of 3D printed parts
22. Real-time defect correction in large-scale polymer additive manufacturing via thermal imaging and laser profilometer10.1016/j.promfg.2020.05.091 ↗
23. Revealing mechanisms of residual stress development in additive manufacturing via digital image correlation
24. Prediction of microstructural defects in additive manufacturing from powder bed quality using digital image correlation10.1016/j.msea.2020.140002 ↗
25. Characterization of in-situ measurements based on layerwise imaging in laser powder bed fusion
26. X-ray attenuation coefficients of elements and mixtures10.1016/0370-1573(81)90014-4 ↗
27. Classification of X-Ray attenuation properties of additive manufacturing and 3D printing materials using computed tomography from 70 to 140 kVp10.3389/fbioe.2021.763960 ↗
28. 3D printing of transparent silicone elastomers
29. M.D. Grapes, MoSyFlow: Motion Synchonized Flow, Lawrence Livermore National Laboratory, 2021.
30. LJ-X8000 Series User’s Manual (3D mode), (n.d.).
31. LJ-X BMP Image to Height Data Conversion, (n.d.).
32. Astropy Collaboration, T.P. Robitaille, E.J. Tollerud, P. Greenfield, M. Droettboom, E. Bray, T. Aldcroft, M. Davis, A. Ginsburg, A.M. Price-Whelan, W.E. Kerzendorf, A. Conley, N. Crighton, K. Barbary, D. Muna, H. Ferguson, F. Grollier, M.M. Parikh, P.H. Nair, H.M. Unther, C. Deil, J. Woillez, S. Conseil, R. Kramer, J.E.H. Turner, L. Singer, R. Fox, B.A. Weaver, V. Zabalza, Z.I. Edwards, K. Azalee Bostroem, D.J. Burke, A.R. Casey, S.M. Crawford, N. Dencheva, J. Ely, T. Jenness, K. Labrie, P.L. Lim, F. Pierfederici, A. Pontzen, A. Ptak, B. Refsdal, M. Servillat, O. Streicher, Astropy: A community Python package for astronomy, Åp. 558 (2013) A33. https://doi.org/10.1051/0004–6361/201322068.10.1051/0004-6361/201322068 ↗
33. Astropy Collaboration, A.M. Price-Whelan, B.M. Sip\Hocz, H.M. Günther, P.L. Lim, S.M. Crawford, S. Conseil, D.L. Shupe, M.W. Craig, N. Dencheva, A. Ginsburg, J.T. Vand erPlas, L.D. Bradley, D. Pérez-Suárez, M. de Val-Borro, T.L. Aldcroft, K.L. Cruz, T.P. Robitaille, E.J. Tollerud, C. Ardelean, T. Babej, Y.P. Bach, M. Bachetti, A.V. Bakanov, S.P. Bamford, G. Barentsen, P. Barmby, A. Baumbach, K.L. Berry, F. Biscani, M. Boquien, K.A. Bostroem, L.G. Bouma, G.B. Brammer, E.M. Bray, H. Breytenbach, H. Buddelmeijer, D.J. Burke, G. Calderone, J.L. Cano Rodríguez, M. Cara, J.V.M. Cardoso, S. Cheedella, Y. Copin, L. Corrales, D. Crichton, D. D’Avella, C. Deil, É. Depagne, J.P. Dietrich, A. Donath, M. Droettboom, N. Earl, T. Erben, S. Fabbro, L.A. Ferreira, T. Finethy, R.T. Fox, L.H. Garrison, S.L.J. Gibbons, D.A. Goldstein, R. Gommers, J.P. Greco, P. Greenfield, A.M. Groener, F. Grollier, A. Hagen, P. Hirst, D. Homeier, A.J. Horton, G. Hosseinzadeh, L. Hu, J.S. Hunkeler, Ž. Ivezić, A. Jain, T. Jenness, G. Kanarek, S. Kendrew, N.S. Kern, W.E. Kerzendorf, A. Khvalko, J. King, D. Kirkby, A.M. Kulkarni, A. Kumar, A. Lee, D. Lenz, S.P. Littlefair, Z. Ma, D.M. Macleod, M. Mastropietro, C. McCully, S. Montagnac, B.M. Morris, M. Mueller, S.J. Mumford, D. Muna, N.A. Murphy, S. Nelson, G.H. Nguyen, J.P. Ninan, M. Nöthe, S. Ogaz, S. Oh, J.K. Parejko, N. Parley, S. Pascual, R. Patil, A.A. Patil, A.L. Plunkett, J.X. Prochaska, T. Rastogi, V. Reddy Janga, J. Sabater, P. Sakurikar, M. Seifert, L.E. Sherbert, H. Sherwood-Taylor, A.Y. Shih, J. Sick, M.T. Silbiger, S. Singanamalla, L.P. Singer, P.H. Sladen, K.A. Sooley, S. Sornarajah, O. Streicher, P. Teuben, S.W. Thomas, G.R. Tremblay, J.E.H. Turner, V. Terrón, M.H. van Kerkwijk, A. de la Vega, L.L. Watkins, B.A. Weaver, J.B. Whitmore, J. Woillez, V. Zabalza, Astropy Contributors, The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package, \aj. 156 (2018) 123. https://doi.org/10.3847/1538–3881/aabc4f.10.3847/1538-3881/aabc4f ↗
34. S. van der Walt, J.L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J.D. Warner, N. Yager, E. Gouillart, T. Yu, the scikit-image contributors, scikit-image: image processing in Python, PeerJ. 2 (2014) e453. https://doi.org/10.7717/peerj.453.10.7717/peerj.453 ↗
35. The OpenCV Library
36. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm10.1016/j.procs.2015.06.090 ↗
37. SLIC superpixels compared to state-of-the-art superpixel methods10.1109/TPAMI.2012.120 ↗
38. K.A.A. Nazeer, M.P. Sebastian, Improving the Accuracy and Efficiency of the k-means Clustering Algorithm, (2009) 5.
39. W. Rasband, Image J., U Natl. Inst. Health Bethesda Md. USA. (2012) //imagej.nih.gov/ij/-//imagej.nih.gov/ij/.
40. The extrusion-based additive manufacturing of moisture-cured silicone elastomer with minimal void for pneumatic actuators
41. Additive manufacturing of silicone structures: a review and prospective
42. A. Roschli, A. Messing, M. Borish, B.K. Post, L.J. Love, ORNL Slicer 2: A Novel Approach for Additive Manufacturing Tool Path Planning, in: University of Texas at Austin, 2017. 〈https://repositories.lib.utexas.edu/handle/2152/89889〉 (accessed April 12, 2022).