0
Journal of Manufacturing Science and Engineering | Volume 139 | Issue 9 | research-article
Research Papers

Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches

[+] Author and Article Information
M. Samie Tootooni, Ashley Dsouza, Ryan Donovan, Peter Borgesen

System Science and Industrial
Engineering Department,
Binghamton University (SUNY),
Binghamton, NY 13902

Prahalad K. Rao

Department of Mechanical
and Materials Engineering,
University of Nebraska–Lincoln,
Lincoln, NE 68588-0452
e-mail: rao@unl.edu

Zhenyu (James) Kong

Grado Department of Industrial
and Systems Engineering,
Virginia Tech,
Blacksburg, VA 24060

1Corresponding author.

Manuscript received February 4, 2017; final manuscript received April 24, 2017; published online June 22, 2017. Assoc. Editor: Zhijian J. Pei.

J. Manuf. Sci. Eng 139(9), 091005 (Jun 22, 2017) (14 pages) Paper No: MANU-17-1073; doi: 10.1115/1.4036641 History: Received February 04, 2017; Revised April 24, 2017
Article
References
Figures
Tables
Citing Articles

The objective of this work is to develop and apply a spectral graph theoretic approach for differentiating between (classifying) additive manufactured (AM) parts contingent on the severity of their dimensional variation from laser-scanned coordinate measurements (3D point cloud). The novelty of the approach is in invoking spectral graph Laplacian eigenvalues as an extracted feature from the laser-scanned 3D point cloud data in conjunction with various machine learning techniques. The outcome is a new method that classifies the dimensional variation of an AM part by sampling less than 5% of the 2 million 3D point cloud data acquired (per part). This is a practically important result, because it reduces the measurement burden for postprocess quality assurance in AM—parts can be laser-scanned and their dimensional variation quickly assessed on the shop floor. To realize the research objective, the procedure is as follows. Test parts are made using the fused filament fabrication (FFF) polymer AM process. The FFF process conditions are varied per a phased design of experiments plan to produce parts with distinctive dimensional variations. Subsequently, each test part is laser scanned and 3D point cloud data are acquired. To classify the dimensional variation among parts, Laplacian eigenvalues are extracted from the 3D point cloud data and used as features within different machine learning approaches. Six machine learning approaches are juxtaposed: sparse representation, k-nearest neighbors, neural network, naïve Bayes, support vector machine, and decision tree. Of these, the sparse representation technique provides the highest classification accuracy (F-score > 97%).
FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

1
Gibson, I. , Rosen, D. W. , and Stucker, B. , 2010, Additive Manufacturing Technologies, Springer, New York.
2
Huang, Y. , Leu, M. C. , Mazumder, J. , and Donmez, A. , 2015, “ Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations,” ASME J. Manuf. Sci. Eng., 137(1), p. 014001. [CrossRef]
3
Rao, P. K. , Kong, Z. , Duty, C. E. , Smith, R. J. , Kunc, V. , and Love, L. J. , 2016, “ Assessment of Dimensional Integrity and Spatial Defect Localization in Additive Manufacturing Using Spectral Graph Theory,” ASME J. Manuf. Sci. Eng., 138(5), p. 051007. [CrossRef]
4
Xu, L. , Huang, Q. , Sabbaghi, A. , and Dasgupta, T. , 2013, “ Shape Deviation Modeling for Dimensional Quality Control in Additive Manufacturing,” ASME Paper No. IMECE2013-66329.
5
Huang, Q. , Zhang, J. , Sabbaghi, A. , and Dasgupta, T. , 2015, “ Optimal Offline Compensation of Shape Shrinkage for Three-Dimensional Printing Processes,” IIE Trans., 47(5), pp. 431–441. [CrossRef]
6
Ameta, G. , Lipman, R. , Moylan, S. , and Witherell, P. , 2015, “ Investigating the Role of Geometric Dimensioning and Tolerancing in Additive Manufacturing,” ASME J. Mech. Des., 137(11), p. 111401. [CrossRef]
7
Witherell, P. , Herron, J. , and Ameta, G. , 2016, “ Towards Annotations and Product Definitions for Additive Manufacturing,” Proc. CIRP, 43, pp. 339–344. [CrossRef]
8
Pal, P. , 2001, “ An Easy Rapid Prototyping Technique With Point Cloud Data,” Rapid Prototyping J., 7(2), pp. 82–90. [CrossRef]
9
Bi, Z. M. , and Wang, L. , 2010, “ Advances in 3D Data Acquisition and Processing for Industrial Applications,” Rob. Comput. Integr. Manuf., 26(5), pp. 403–413. [CrossRef]
10
Iuliano, L. , and Minetola, P. , 2008, “ Enhancing Moulds Manufacturing by Means of Reverse Engineering,” Int. J. Adv. Manuf. Technol., 43(5), pp. 551–562.
11
Savio, E. , De Chiffre, L. , and Schmitt, R. , 2007, “ Metrology of Freeform Shaped Parts,” CIRP Ann. Manuf. Technol., 56(2), pp. 810–835. [CrossRef]
12
Kruth, J. P. , Bartscher, M. , Carmignato, S. , Schmitt, R. , De Chiffre, L. , and Weckenmann, A. , 2011, “ Computed Tomography for Dimensional Metrology,” CIRP Ann. Manuf. Technol., 60(2), pp. 821–842. [CrossRef]
13
Raja, V. , Zhang, S. , Garside, J. , Ryall, C. , and Wimpenny, D. , 2006, “ Rapid and Cost-Effective Manufacturing of High-Integrity Aerospace Components,” Int. J. Adv. Manuf. Technol., 27(7–8), pp. 759–773. [CrossRef]
14
Ahn, D. , Kweon, J.-H. , Kwon, S. , Song, J. , and Lee, S. , 2009, “ Representation of Surface Roughness in Fused Deposition Modeling,” J. Mater. Process. Technol., 209(15–16), pp. 5593–5600. [CrossRef]
15
Galantucci, L. , Lavecchia, F. , and Percoco, G. , 2009, “ Experimental Study Aiming to Enhance the Surface Finish of Fused Deposition Modeled Parts,” CIRP Ann.-Manuf. Technol., 58(1), pp. 189–192. [CrossRef]
16
Anitha, R. , Arunachalam, S. , and Radhakrishnan, P. , 2001, “ Critical Parameters Influencing the Quality of Prototypes in Fused Deposition Modelling,” J. Mater. Process. Technol., 118(1), pp. 385–388. [CrossRef]
17
Steuben, J. , Van Bossuyt, D. L. , and Turner, C. , 2015, “ Design for Fused Filament Fabrication Additive Manufacturing,” ASME Paper No. DETC2015-46355.
18
Lanzotti, A. , Martorelli, M. , and Staiano, G. , 2015, “ Understanding Process Parameter Effects of RepRap Open-Source Three-Dimensional Printers Through a Design of Experiments Approach,” ASME J. Manuf. Sci. Eng., 137(1), p. 011017. [CrossRef]
19
Kainat, M. , Adeeb, S. , Cheng, J. R. , Ferguson, J. , and Martens, M. , 2012, “ Identifying Initial Imperfection Patterns of Energy Pipes Using a 3D Laser Scanner,” ASME Paper No. IPC2012-90201.
20
Jiang, X. , Scott, P. , and Whitehouse, D. , 2007, “ Freeform Surface Characterisation—A Fresh Strategy,” CIRP Ann. Manuf. Technol., 56(1), pp. 553–556. [CrossRef]
21
Arrieta, C. , 2012, “ Quantitative Assessments of Geometric Errors for Rapid Prototyping in Medical Applications,” Rapid Prototyping J., 18(6), pp. 431–442. [CrossRef]
22
Bouyssie, J. , Bouyssie, S. , Sharrock, P. , and Duran, D. , 1997, “ Stereolithographic Models Derived From X-Ray Computed Tomography Reproduction Accuracy,” Surg. Radiol. Anat., 19(3), pp. 193–199. [PubMed]
23
Huang, Q. , 2016, “ An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deformation in Additive Manufacturing,” ASME J. Manuf. Sci. Eng., 138(6), p. 061010. [CrossRef]
24
Jin, Y. , Joe Qin, S. , and Huang, Q. , 2016, “ Offline Predictive Control of Out-of-Plane Shape Deformation for Additive Manufacturing,” ASME J. Manuf. Sci. Eng., 138(12), p. 121005. [CrossRef]
25
Xu, K. , and Chen, Y. , 2015, “ Mask Image Planning for Deformation Control in Projection-Based Stereolithography Process,” ASME J. Manuf. Sci. Eng., 137(3), p. 031014. [CrossRef]
26
Cooke, A. , and Soons, J. , 2010, “ Variability in the Geometric Accuracy of Additively Manufactured Test Parts,” 21st Annual International Solid Freeform Fabrication Symposium: An Additive Manufacturing Conference (SFF), Austin, TX, Aug. 9–11. https://sffsymposium.engr.utexas.edu/Manuscripts/2010/2010-01-Cooke.pdf
27
Dsouza, A. , 2016, “ Experimental Evolutionary Optimization of Geometric Integrity in Fused Filament Fabrication (FFF) Additive Manufacturing (AM) Process,” M.S. thesis, Binghamton University, Binghamton, NY. http://gradworks.umi.com/10/13/10137420.html
28
Sabourin, E. , Houser, S. A. , and Helge Bøhn, J. , 1997, “ Accurate Exterior, Fast Interior Layered Manufacturing,” Rapid Prototyping J., 3(2), pp. 44–52. [CrossRef]
29
Huang, J. , and Menq, C.-H. , 2002, “ Automatic CAD Model Reconstruction From Multiple Point Clouds for Reverse Engineering,” ASME J. Comput. Inf. Sci. Eng., 2(3), pp. 160–170. [CrossRef]
30
Woo, H. , Kang, E. , Wang, S. , and Lee, K. H. , 2002, “ A New Segmentation Method for Point Cloud Data,” Int. J. Mach. Tools Manuf., 42(2), pp. 167–178. [CrossRef]
31
Schnabel, R. , Wahl, R. , and Klein, R. , 2007, “ Efficient RANSAC for Point-Cloud Shape Detection,” Comput. Graphics Forum, 26(2), pp. 214–226. [CrossRef]
32
Rao, P. K. , Beyca, O. F. , Kong, Z. , Bukkaptanam, S. T. , Case, K. E. , and Komanduri, R. , 2015, “ A Graph Theoretic Approach for Quantification of Surface Morphology and Its Application to Chemical Mechanical Planarization (CMP) Process,” IIE Trans., 47(10), pp. 1088–1111. [CrossRef]
33
Chung, F. R. K. , 1997, Spectral Graph Theory, American Mathematical Society, Providence, RI.
34
Shuman, D. I. , Narang, S. K. , Frossard, P. , Ortega, A. , and Vandergheynst, P. , 2013, “ The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains,” IEEE Signal Process. Mag., 30(3), pp. 83–98. [CrossRef]
35
Shuman, D. I. , Ricaud, B. , and Vandergheynst, P. , 2012, “ A Windowed Graph Fourier Transform,” IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, Aug. 5–8, pp. 133–136.
36
Tootooni, M. S. , Rao, P. K. , Chou, C.-A. , and Kong, Z. J. , 2016, “ A Spectral Graph Theoretic Approach for Monitoring Multivariate Time Series Data From Complex Dynamical Processes,” IEEE Trans. Autom. Sci. Eng., PP(99), pp. 1–18.
37
Bastani, K. , Rao, P. K. , and Kong, Z. , 2016, “ An Online Sparse Estimation-Based Classification Approach for Real-Time Monitoring in Advanced Manufacturing Processes From Heterogeneous Sensor Data,” IIE Trans., 48(7), pp. 579–598. [CrossRef]
38
Tibshirani, R. , 1996, “ Regression Shrinkage and Selection Via the LASSO,” J. R. Stat. Soc., Ser. B, 58(1), pp. 267–288. https://statweb.stanford.edu/~tibs/lasso/lasso.pdf
39
Tropp, J. A. , and Gilbert, A. C. , 2007, “ Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit,” IEEE Trans. Inf. Theory, 53(12), pp. 4655–4666. [CrossRef]
40
Tipping, M. E. , 2001, “ Sparse Bayesian Learning and the Relevance Vector Machine,” J. Mach. Learn. Res., 1, pp. 211–244.
41
Zhan, C. , Chen, G. , and Yeung, L. F. , 2010, “ On the Distributions of Laplacian Eigenvalues Versus Node Degrees in Complex Networks,” Physica A, 389(8), pp. 1779–1788. [CrossRef]
42
Bishop, C. M. , 2006, Pattern Recognition and Machine Learning, Springer, New York.
43
Hagan, M. T. , Demuth, H. B. , Beale, M. H. , and De Jesús, O. , 1996, Neural Network Design, PWS Publishing Company, Boston, MA.
44
Fürnkranz, J. , 2002, “ Round Robin Classification,” J. Mach. Learn. Res., 2, pp. 721–747. http://www.jmlr.org/papers/volume2/fuernkranz02a/fuernkranz02a.pdf

Figures

Grahic Jump Location
Fig. 1

Flooded contour plots (dimensional deviation maps) of the test artifact used in FFF experiments detailed in Sec. 3.2. The material is ABS polymer. The first row (1) shows the top views and the second row (2) contains the bottom views of the parts. Figures (a)–(d) represent different parts printed under infill percentages of 70%, 80%, 90%, and 100% at 230 °C, respectively. The color scale shown on the right is in millimeter. The dimensional variations progressively increase with infill percentage (see color figure online).

+Flooded contour plots (dimensional deviation maps) of the test artifact used in FFF experiments detailed in Sec. 3.2. The material is ABS polymer. The first row (1) shows the top views and the second row (2) contains the bottom views of the parts. Figures (a)–(d) represent different parts printed under infill percentages of 70%, 80%, 90%, and 100% at 230 °C, respectively. The color scale shown on the right is in millimeter. The dimensional variations progressively increase with infill percentage (see color figure online).
View Large  |  Save Figure  |  Download Slide (.ppt)
Flooded contour plots (dimensional deviation maps) of the test artifact used in FFF experiments detailed in Sec. 3.2. The material is ABS polymer. The first row (1) shows the top views and the second row (2) contains the bottom views of the parts. Figures (a)–(d) represent different parts printed under infill percentages of 70%, 80%, 90%, and 100% at 230 °C, respectively. The color scale shown on the right is in millimeter. The dimensional variations progressively increase with infill percentage (see color figure online).
Grahic Jump Location
Fig. 2

Design of the circle–square–diamond part—a simplified embodiment of the NAS 979 standard test artifact for testing accuracy of machining centers. The dimensions are in millimeters. Figures (a) and (b) are front and top views of the part, respectively, and figure (c) is an isometric projection of the part (The NAS 979 standard artifact was first used in the AM context by Cooke and Soons [26]).

+Design of the circle–square–diamond part—a simplified embodiment of the NAS 979 standard test artifact for testing accuracy of machining centers. The dimensions are in millimeters. Figures (a) and (b) are front and top views of the part, respectively, and figure (c) is an isometric projection of the part (The NAS 979 standard artifact was first used in the AM context by Cooke and Soons [26]).
View Large  |  Save Figure  |  Download Slide (.ppt)
Design of the circle–square–diamond part—a simplified embodiment of the NAS 979 standard test artifact for testing accuracy of machining centers. The dimensions are in millimeters. Figures (a) and (b) are front and top views of the part, respectively, and figure (c) is an isometric projection of the part (The NAS 979 standard artifact was first used in the AM context by Cooke and Soons [26]).
Grahic Jump Location
Fig. 6

Random samples taken from deviation coordinates of two FFF parts are mapped into unweighted, undirected graphs. These graphs are from parts printed with (a) 90% infill at 230 °C temperature and (b) 70% infill at 230 °C temperature. The distinctive connectivity structures of these graphs are indicative of the differences in geometric variation between parts created at 90% and 70% infill.

+Random samples taken from deviation coordinates of two FFF parts are mapped into unweighted, undirected graphs. These graphs are from parts printed with (a) 90% infill at 230 °C temperature and (b) 70% infill at 230 °C temperature. The distinctive connectivity structures of these graphs are indicative of the differences in geometric variation between parts created at 90% and 70% infill.
View Large  |  Save Figure  |  Download Slide (.ppt)
Random samples taken from deviation coordinates of two FFF parts are mapped into unweighted, undirected graphs. These graphs are from parts printed with (a) 90% infill at 230 °C temperature and (b) 70% infill at 230 °C temperature. The distinctive connectivity structures of these graphs are indicative of the differences in geometric variation between parts created at 90% and 70% infill.
Grahic Jump Location
Fig. 3

The eight points used for alignment of the scan points with the CAD model

+The eight points used for alignment of the scan points with the CAD model
View Large  |  Save Figure  |  Download Slide (.ppt)
The eight points used for alignment of the scan points with the CAD model
Grahic Jump Location
Fig. 4

The design of experiments matrix and resulting RMS and in-tolerance percentage at different settings

+The design of experiments matrix and resulting RMS and in-tolerance percentage at different settings
View Large  |  Save Figure  |  Download Slide (.ppt)
The design of experiments matrix and resulting RMS and in-tolerance percentage at different settings
Grahic Jump Location
Fig. 5

Micrograph showing the effect of infill percentage on the internal morphology of the circle–square–diamond part. Shown is the quarter cross section of the part; the circular section is at the top end. At 100% infill, the thermal residual stresses cannot be accommodated without deleteriously affecting the dimensional integrity since there is no vacant space for stress relief.

+Micrograph showing the effect of infill percentage on the internal morphology of the circle–square–diamond part. Shown is the quarter cross section of the part; the circular section is at the top end. At 100% infill, the thermal residual stresses cannot be accommodated without deleteriously affecting the dimensional integrity since there is no vacant space for stress relief.
View Large  |  Save Figure  |  Download Slide (.ppt)
Micrograph showing the effect of infill percentage on the internal morphology of the circle–square–diamond part. Shown is the quarter cross section of the part; the circular section is at the top end. At 100% infill, the thermal residual stresses cannot be accommodated without deleteriously affecting the dimensional integrity since there is no vacant space for stress relief.
Grahic Jump Location
Fig. 7

(a) A fully connected representative graph, with the dark and light nodes analogous to different dimensional variations on the part. Edge distances are estimated using the radial basis function (Eq. (4)). (b) Edges connecting similar nodes are identified with dashed lines using the threshold function (Eq. (5)). (c) Edges connecting similar textured nodes are pruned [32].

+(a) A fully connected representative graph, with the dark and light nodes analogous to different dimensional variations on the part. Edge distances are estimated using the radial basis function (Eq. (4)). (b) Edges connecting similar nodes are identified with dashed lines using the threshold function (Eq. (5)). (c) Edges connecting similar textured nodes are pruned [32].
View Large  |  Save Figure  |  Download Slide (.ppt)
(a) A fully connected representative graph, with the dark and light nodes analogous to different dimensional variations on the part. Edge distances are estimated using the radial basis function (Eq. (4)). (b) Edges connecting similar nodes are identified with dashed lines using the threshold function (Eq. (5)). (c) Edges connecting similar textured nodes are pruned [32].
Grahic Jump Location
Fig. 8

Scenario I sampling of point cloud data. In scenario I sampling, the circle–square–diamond part is divided into nine areas: (a) top view and (b) 3D view.

+Scenario I sampling of point cloud data. In scenario I sampling, the circle–square–diamond part is divided into nine areas: (a) top view and (b) 3D view.
View Large  |  Save Figure  |  Download Slide (.ppt)
Scenario I sampling of point cloud data. In scenario I sampling, the circle–square–diamond part is divided into nine areas: (a) top view and (b) 3D view.
Grahic Jump Location
Fig. 9

Three-dimensional view of the designed part in scenario II sampling approach; in this scenario, the XY planes considered as the four areas. Samples are taken at different Z-heights (build direction).

+Three-dimensional view of the designed part in scenario II sampling approach; in this scenario, the XY planes considered as the four areas. Samples are taken at different Z-heights (build direction).
View Large  |  Save Figure  |  Download Slide (.ppt)
Three-dimensional view of the designed part in scenario II sampling approach; in this scenario, the XY planes considered as the four areas. Samples are taken at different Z-heights (build direction).

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Introduction
Axial-Flow Compressors> Chapter 1
Outlook
Closed-Cycle Gas Turbines> Chapter 11
Introduction and Definitions
Handbook on Stiffness & Damping in Mechanical Design> Chapter 1
Interface Design of Man-Machine Engineering System Based on SkinMagic
Proceedings of the International Conference on Technology Management and Innovation
Design of Rotor Time Constant Estimator and Sliding Mode Position Controller for Induction Machines
Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010)
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy. | Accept
Sign In