Graphical Abstract Figure
Abstract
Defects shape, volume, and orientation all have a direct impact on the mechanical properties of Laser Powder Bed Fused (L-PBF-ed) parts. Therefore, it is necessary to evaluate and analyze the three-dimensional (3D) geometrical characteristics of these defects. X-ray Computed Tomography (XCT) can reveal an object's internal structure by volumetric scanning through its building direction. Point clouds are 3D data that can be extracted from the stack of XCT images taken from a part to perform further analysis. This study presents a novel approach for 3D segmentation and geometrical analysis of L-PBF defect structures from XCT images. The proposed method integrates Voronoi labeling and 3D point cloud reconstruction to reveal individual defect characteristics from the XCT image stack of a part. A case study showed the proposed methodology's effectiveness in identifying and characterizing defect regions in L-PBF-ed Cobalt-Chrome (CoCr) parts.
References
1.
Diener
, S.
, Zocca
, A.
, and Günster
, J.
, 2021
, “Literature Review: Methods for Achieving High Powder bed Densities in Ceramic Powder Bed Based Additive Manufacturing
,” Open Ceram.
, 8
, pp. 100191
–100191
. 2.
Mahmoud
, D.
, Magolon
, M.
, Boer
, J.
, Elbestawi
, M. A.
, and Mohammadi
, M. G.
, 2021
, “Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review
,” Appl. Sci.
, 11
(24
), pp. 11910
–11910
. 3.
Kim
, F. H.
, Moylan
, S. P.
, Garboczi
, E. J.
, and Slotwinski
, J. A.
, 2017
, “Investigation of Pore Structure in Cobalt Chrome Additively Manufactured Parts Using X-ray Computed Tomography and Three-Dimensional Image Analysis
,” Addit. Manuf.
, 17
, pp. 23
–38
. 4.
Grasso
, M.
, and Colosimo
, B. M.
, 2017
, “Process Defects and in Situ Monitoring Methods in Metal Powder Bed Fusion: A Review
,” Meas. Sci. Technol.
, 28
(4
), pp. 44005
–44005
. 5.
Al-Maharma
, A. Y.
, Patil
, S. P.
, and Markert
, B.
, 2020
, “Effects of Porosity on the Mechanical Properties of Additively Manufactured Components: A Critical Review
,” Mater. Res. Express
, 7
(12
), pp. 122001
–122001
. 6.
Desrosiers
, C.
, Letenneur
, M.
, Bernier
, F.
, Cheriet
, F.
, Brailovski
, V.
, Piché
, N.
, and Guibault
, F.
, 2022
, “Correlative Laser Confocal Microscopy Study and Multimodal 2D/3D Registration as Ground Truth for X-Ray Inspection of Internal Defects in LPBF Manufacturing
,” 11th Conference on Industrial Computed Tomography (iCT), Wels, Austria, Feb. 8–11, e-J. Nondestruct. Test.
, 27
(3
). 7.
Thompson
, A.
, Maskery
, I.
, and Leach
, R. K.
, 2016
, “X-Ray Computed Tomography for Additive Manufacturing: A Review
,” Meas. Sci. Technol.
, 27
(7
), pp. 72001
–72001
. 8.
Taud
, H.
, Martinez-Angeles
, R.
, Parrot
, J. F.
, and Hernandez-Escobedo
, L.
, 2005
, “Porosity Estimation Method by X-Ray Computed Tomography
,” J. Pet. Sci. Eng.
, 47
(3–4
), pp. 209
–217
. 9.
Szeliski
, R.
, 2011
, “Segmentation,” in Computer Vision,” Texts in Computer Science
, Springer
, New York
, pp. 235
–271
.10.
Poudel
, A.
, Yasin
, M. S.
, Ye
, J.
, Liu
, J.
, Vinel
, A.
, Shao
, S.
, and Shamsaei
, N.
, 2022
, “Feature-Based Volumetric Defect Classification in Metal Additive Manufacturing
,” Nat. Commun.
, 13
(1
), pp. 6369
–6369
. 11.
Snell
, R.
, Tammas-Williams
, S.
, Chechik
, L.
, Lyle
, A.
, Hernández-Nava
, E.
, Boig
, C.
, Panoutsos
, G.
, and Todd
, I.
, 2020
, “Methods for Rapid Pore Classification in Metal Additive Manufacturing
,” JOM
, 72
(1
), pp. 101
–109
. 12.
Ye
, J.
, Poudel
, A.
, Liu
, J.
, Vinel
, A.
, Silva
, D.
, Shao
, S.
, and Shamsaei
, N.
, 2023
, “Machine Learning Augmented X-Ray Computed Tomography Features for Volumetric Defect Classification in Laser Beam Powder bed Fusion
,” Int. J. Adv. Manuf. Technol.
, 126
(7–8
), pp. 3093
–3107
. 13.
Gobert
, C.
, Kudzal
, A.
, Sietins
, J.
, Mock
, C.
, Sun
, J.
, and Mcwilliams
, B.
, 2020
, “Porosity Segmentation in X-Ray Computed Tomography Scans of Metal Additively Manufactured Specimens With Machine Learning
,” Addit. Manuf.
, 36
, pp. 101460
–101460
. 14.
Mehta
, M.
, and Shao
, C.
, 2022
, “Federated Learning-Based Semantic Segmentation for Pixel-Wise Defect Detection in Additive Manufacturing
,” J. Manuf. Syst.
, 64
, pp. 197
–210
. 15.
Wong
, V. W. H.
, Ferguson
, M.
, Law
, K. H.
, Lee
, Y.-T. T.
, and Witherell
, P.
, 2021
, “Automatic Volumetric Segmentation of Additive Manufacturing Defects With 3D U-Net
”, doi: 10.48550/ARXIV.2101.08993.16.
Bhanu
, B.
, Lee
, S. K.
, Ho
, C. C.
, and Henderson
, T. C.
, 1986
, “Range Data Processing: Representation of Surfaces by Edges,” The Eighth International Conference on Pattern Recognition
, IEEE Computer Society Press
, Piscataway, NJ
, Vol. 236, p. 238
.17.
Wani
, M. A.
, and Arabnia
, H. R.
, 2003
, “Parallel Edge-Region-Based Segmentation Algorithm Targeted at Reconfigurable Multi-Ring Network
,” J. Supercomput.
, 25
(1
), pp. 43
–62
. 18.
Sappa
, A. D.
, and Devy
, M.
, 2001
, “Fast Range Image Segmentation by an Edge Detection Strategy
,” Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling
, Quebec City, QC, Canada
, IEEE Computer Society, pp. 292
–299
.19.
Filin
, S.
, 2002
, “Surface Clustering From Airborne Laser Scanning Data
,” Proceedings of the International Archives of Photogrammetry and Remote Sensing
, Graz, Austria
, Sept. 9–13
, Vol. 32(A), pp. 119
–124
.20.
Golovinskiy
, A.
, and Funk
, T.
, 2009
, “Min-cut Based Segmentation of Point Clouds
,” Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops
, Kyoto, Japan
, IEEE, pp. 39
–46
.21.
Lu
, X.
, Yao
, J.
, Tu
, J.
, Li
, K.
, Li
, L.
, and Liu
, Y.
, 2016
, “Pairwise Linkage for Point Cloud Segmentation
,” ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.
, 3
, pp. 201
–208
. 22.
Su
, H.
, Maji
, S.
, Kalogerakis
, E.
, and Learned-Miller
, E.
, 2015
, “Multi-View Convolutional Neural Networks for 3D Shape Recognition
,” Proceedings of the IEEE International Conference on Computer Vision
, Santiago, Chile
, pp. 945
–953
.23.
Kumawat
, S.
, and Raman
, S.
, 2019
, “LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks
,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
, Long Beach, CA
, pp. 4903
–4912
.24.
Feng
, Y.
, Zhang
, Z.
, Zhao
, X.
, Ji
, R.
, and Gao
, Y.
, 2018
, “GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition
,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
, Salt Lake City, UT
, pp. 264
–272
.25.
Zhang
, J.
, Lin
, X.
, and Ning
, X.
, 2013
, “SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas
,” Remote Sens.
, 5
(8
), pp. 3749
–3775
. 26.
Zhang
, J.
, Zhao
, X.
, Chen
, Z.
, and Lu
, Z.
, 2019
, “A Review of Deep Learning-Based Semantic Segmentation for Point Cloud
,” IEEE Access
, 7
, pp. 179118
–179133
. 27.
Li
, Y.
, Wang
, Y.
, and Liu
, Y.
, 2023
, “Three-Dimensional Point Cloud Segmentation Based on Context Feature for Sheet Metal Part Boundary Recognition
,” IEEE Trans. Instrum. Meas.
, 72
, pp. 1
–10
. 28.
Mémoli
, F.
, and Sapiro
, G.
, 2004
, “Comparing Point Clouds
,” Proceedings of the 2004 Eurographics/ACM SIG- GRAPH Symposium on Geometry Processing, ACM
, Nice, France
, pp. 32
–40
.29.
He
, Y.
, Yu
, H.
, Liu
, X.
, Yang
, Z.
, Sun
, W.
, and Mian
, A.
, 2021
, “Deep Learning Based 3D Segmentation: A Survey
”. 30.
Chisena
, R. S.
, Engstrom
, S. M.
, and Shih
, A. J.
, 2020
, “Automated Thresholding Method for the Computed Tomography Inspection of the Internal Composition of Parts Fabricated Using Additive Manufacturing
,” Addit. Manuf.
, 33
, pp. 101185
–101185
. 31.
Bellens
, S.
, Probst
, G. M.
, Janssens
, M.
, Vandewalle
, P.
, and Dewulf
, W.
, 2022
, “Evaluating Conventional and Deep Learning Segmentation for Fast X-Ray CT Porosity Measurements of Polymer Laser Sintered AM Parts
,” Polym. Test.
, 110
, pp. 107540
–107540
. 32.
Zhu
, N.
, Wang
, G.
, Yang
, G.
, and Dai
, W.
, 2009
, “A Fast 2D Otsu Thresholding Algorithm Based on Improved Histogram
,” Proceedings of the 2009 Chinese Conference on Pattern Recognition
, Nanjing, China
, IEEE, pp. 1
–5
. 33.
Ouidadi
, H.
, Xu
, B.
, and Guo
, S.
, “Defect Segmentation From X-Ray Computed Tomography of Laser Powder Bed Fusion Parts: A Comparative Study Among Machine Learning, Deep Learning, and Statistical Image Thresholding Methods
,” Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference. 1: Additive Manufacturing; Advanced Materials Manufacturing; Biomanufacturing; Life Cycle Engineering
, New Brunswick, NJ
, June 12–16
, p. V001T01A012
, ASME. 34.
Feng
, S.
, Chen
, Z.
, Bircher
, B.
, Ji
, Z.
, Nyborg
, L.
, and Bigot
, S.
, 2022
, “Predicting Laser Powder Bed Fusion Defects Through In-Process Monitoring Data and Machine Learning
,” Mater. Des.
, 222
. 35.
Gordon
, J. V.
, Narra
, S. P.
, Cunningham
, R. W.
, Liu
, H.
, Chen
, H.
, Suter
, R. M.
, Beuth
, J. L.
, and Rollett
, A. D.
2020
, “Defect Structure Process Maps for Laser Powder Bed Fusion Additive Manufacturing
,” Addit. Manuf.
, 36
, p. 101552
. 36.
Thiede
, T.
, Mishurova
, T.
, Evsevleev
, S.
, Serrano-Munoz
, I.
, Gollwitzer
, C.
, and Bruno
, G.
, 2019
, “3D Shape Analysis of Powder for Laser Beam Melting by Synchrotron X-Ray CT
,” Quantum Beam Sci.
, 3
(1
), pp. 3
–3
. 37.
clEsperanto Library
, “Voronoi Otsu Labeling
.” https://github.com/clEsperanto/pyclesperanto_prototype/blob/master/pyclesperanto_prototype/_tier9/_voronoi_otsu_labeling.py.38.
Shah
, A.
, 2022
, “Comparative Analysis of Median Filter and Its Variants for Removal of Impulse Noise From Gray Scale Images
,” J. King Saud Univ. Comput. Inf. Sci.
, 34
(3
), pp. 505
–519
. 39.
Holub
, O.
, and Ferreira
, S. T.
, 2006
, “Quantitative Histogram Analysis of Images
,” Comput. Phys. Commun.
, 175
(9
), pp. 620
–623
. 40.
Dhanachandra
, N.
, Manglem
, K.
, and Chanu
, Y. J.
, 2015
, “Image Segmentation Using K-Means Clustering Algorithm and Subtractive Clustering Algorithm
,” Proc. Comput. Sci.
, 54
, pp. 764
–771
. 41.
Wiener
, M.
, Turkeltaub
, P.
, and Coslett
, H. B.
, 2010
, “The Image of Time: A Voxel-Wise Meta-Analysis
,” NeuroImage
, 49
(2
), pp. 1728
–1740
. 42.
Masters
, B. R.
, Gonzalez
, R. C.
, and Woods
, R. E.
, and, 2009
, “Book Review: Digital Image Processing, Third Edition
,” J. Biomed. Opt.
, 14
(2
), p. 029901
. 43.
“The Gaussian Kernel”
. https://pages.stat.wisc.edu/~mchung/teaching/MIA/reading/diffusion.gaussian.kernel.pdf.pdf.44.
Otsu
, N.
, 1979
, “A Threshold Selection Method From Gray-Level Histograms
,” IEEE Trans. Syst. Man Cybern.
, 9
(1
), pp. 62
–66
. 45.
Grilli
, E.
, Menna
, F.
, and Remondino
, F.
, 2017
, “A Review of Point Clouds Segmentation and Classification Algorithms
,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
, XLII-2/W3
, pp. 339
–344
. 46.
Ester
, M.
, Kriegel
, H.
, Sander
, J.
, and Xu
, X.
, 1996
, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases With Noise
,” kdd
, 96
, pp. 226
–231
.47.
Schubert
, E.
, Sander
, J.
, Ester
, M.
, Kriegel
, H. P.
, and Xu
, X.
, 2017
, “DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN
,” ACM Trans. Database Syst.
, 42
(3
), pp. 1
–21
. 48.
Kim
, F. H.
, Garboczi
, E. J.
, Moylan
, S. P.
, and Slotwinski
, J.
, 2019
, “High-Resolution X-Ray Computed Tomography (XCT) Image Data Set of Additively Manufactured Cobalt Chrome Samples Produced With Varying Laser Powder Bed Fusion Processing Parameters
”. https://www.nist.gov/el/intelligent-systems-division-73500/cocr-am-xct-data.49.
Khanzadeh
, M.
, Chowdhury
, S.
, Marufuzzaman
, M.
, Tschopp
, M. A.
, and Bian
, L.
, 2018
, “Porosity Prediction: Supervised-Learning of Thermal History for Direct Laser Deposition
,” J. Manuf. Syst.
, 47
, pp. 69
–82
. 50.
Aranganayagi
, S.
, and Thangavel
, K.
, 2007
, “Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure
,” Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007)
, Sivakasi, India
, IEEE, pp. 13
–17
. 
