Research Papers

Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

[+] Author and Article Information
Mojtaba Khanzadeh, Ruholla Jafari-Marandi, Brian K. Smith

Industrial and Systems Engineering Department,
Mississippi State University,
Starkville, MS 39759

Prahalada Rao

Department of Mechanical and
Materials Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588

Mark A. Tschopp

U.S. Army Research Laboratory,
Aberdeen Proving Ground,
Hillandale, MD 21005

Linkan Bian

Industrial and Systems Engineering Department,
Mississippi State University,
Starkville, MS 39759
e-mail: bian@ise.msstate.edu

1Corresponding author.

Manuscript received June 11, 2017; final manuscript received November 19, 2017; published online December 21, 2017. Assoc. Editor: Zhijian J. Pei. This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Manuf. Sci. Eng 140(3), 031011 (Dec 21, 2017) (12 pages) Paper No: MANU-17-1366; doi: 10.1115/1.4038598 History: Received June 11, 2017; Revised November 19, 2017

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

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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]
Gao, W. , Zhang, Y. , Ramanujan, D. , Ramani, K. , Chen, Y. , Williams, C. B. , Wang, C. C. , Shin, Y. C. , Zhang, S. , and Zavattieri, P. D. , 2015, “ The Status, Challenges, and Future of Additive Manufacturing in Engineering,” Comput.-Aided Des., 69, pp. 65–89. [CrossRef]
Thompson, S. M. , Bian, L. , Shamsaei, N. , and Yadollahi, A. , 2015, “ An Overview of Direct Laser Deposition for Additive Manufacturing—Part I: Transport Phenomena, Modeling and Diagnostics,” Addit. Manuf., 8, pp. 36–62. [CrossRef]
Ameta, G. , Witherell, P. , Moylan, S. , and Lipman, R. , 2015, “Tolerance Specification and Related Issues for Additively Manufactured Products,” ASME Paper No. DETC2015-47531.
Mahesh, M. , Wong, Y. , Fuh, J. , and Loh, H. , 2004, “ Benchmarking for Comparative Evaluation of RP Systems and Processes,” Rapid Prototyping J., 10(2), pp. 123–135.
Tahan, S. A. , and Levesque, S. , 2009, “ Exploiting the Process Capability of Profile Tolerance According GD&T ASME-Y14.5M,” IEEE International Conference on Computers and Industrial Engineering (ICCIE), Troyes, France, July 6–9, pp. 1285–1290.
Criminisi, A. , Reid, I. , and Zisserman, A. , 2000, “ Single View Metrology,” Int. J. Comput. Vision, 40(2), pp. 123–148. [CrossRef]
Hiller, J. , and Hornberger, P. , 2016, “ Measurement Accuracy in X-Ray Computed Tomography Metrology: Toward a Systematic Analysis of Interference Effects in Tomographic Imaging,” Precision Eng., 45, pp. 18–32. [CrossRef]
Davidson, J. K. , Shah, J. J. , and Mujezinovié, A. , 2005, “Method and Apparatus for Geometric Variations to Integrate Parametric Computer Aided Design With Tolerance Analyses and Optimization,” U.S. Patent No. 6,963,824. https://www.google.com/patents/US6963824
Malkin, R. , Caleap, M. , and Drinkwater, B. W. , 2016, “ A Numerical Database for Ultrasonic Defect Characterisation Using Array Data: Robustness and Accuracy,” NDT&E Int., 83, pp. 94–103. [CrossRef]
Schleich, B. , Anwer, N. , Mathieu, L. , and Wartzack, S. , 2014, “ Skin Model Shapes: A New Paradigm Shift for Geometric Variations Modelling in Mechanical Engineering,” Comput.-Aided Des., 50, pp. 1–15. [CrossRef]
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]
Zhou, J. G. , Herscovici, D. , and Chen, C. C. , 2000, “ Parametric Process Optimization to Improve the Accuracy of Rapid Prototyped Stereolithography Parts,” Int. J. Mach. Tools Manuf., 40(3), pp. 363–379. [CrossRef]
Campanelli, S. , Cardano, G. , Giannoccaro, R. , Ludovico, A. , and Bohez, E. L. , 2007, “ Statistical Analysis of the Stereolithographic Process to Improve the Accuracy,” Comput.-Aided Des., 39(1), pp. 80–86. [CrossRef]
Onuh, S. , and Hon, K. , 2001, “ Improving Stereolithography Part Accuracy for Industrial Applications,” Int. J. Adv. Manuf. Technol., 17(1), pp. 61–68. [CrossRef]
Chen, H. , Chen, H. , Zhao, Y. F. , and Zhao, Y. F. , 2016, “ Process Parameters Optimization for Improving Surface Quality and Manufacturing Accuracy of Binder Jetting Additive Manufacturing Process,” Rapid Prototyping J., 22(3), pp. 527–538. [CrossRef]
Wang, R.-J. , Wang, L. , Zhao, L. , and Liu, Z. , 2007, “ Influence of Process Parameters on Part Shrinkage in SLS,” Int. J. Adv. Manuf. Technol., 33(5), pp. 498–504. [CrossRef]
Noriega, A. , Blanco, D. , Alvarez, B. , and Garcia, A. , 2013, “ Dimensional Accuracy Improvement of FDM Square Cross-Section Parts Using Artificial Neural Networks and an Optimization Algorithm,” Int. J. Adv. Manuf. Technol., 69(9–12), pp. 2301–2313. [CrossRef]
Gao, J. , Gindy, N. , and Clark, D. , 2005, “ Extraction/Conversion of Geometric Dimensions and Tolerances for Machining Features,” Int. J. Adv. Manuf. Technol., 26(4), pp. 405–414. [CrossRef]
Khanzadeh, M. , Marandi, R. J. , Tootooni, M. S. , Bian, L. , Smith, B. , and Rao, P. , 2016, “ Profiling and Optimizing the Geometric Accuracy of Additively Manufactured Components Via Self-Organizing Map,” Annual International Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 8–10, pp. 1303–1313. https://www.researchgate.net/profile/Mojtaba_Khanzadeh/publication/311911974_Profiling_and_Optimizing_the_Geometric_Accuracy_of_Additively_Manufactured_Components_via_Self-Organizing_Map/links/5861f17008ae8fce4907002f/Profiling-and-Optimizing-the-Geometric-Accuracy-of-Additively-Manufactured-Components-via-Self-Organizing-Map.pdf
King, W. , Anderson, A. , Ferencz, R. , Hodge, N. , Kamath, C. , Khairallah, S. , and Rubenchik, A. , 2015, “ Laser Powder Bed Fusion Additive Manufacturing of Metals; Physics, Computational, and Materials Challenges,” Appl. Phys. Rev., 2(4), p. 041304. [CrossRef]
Pal, D. , Patil, N. , Nikoukar, M. , Zeng, K. , Kutty, K. H. , and Stucker, B. E. , 2013, “ An Integrated Approach to Cyber-Enabled Additive Manufacturing Using Physics Based, Coupled Multi-Scale Process Modeling,” Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 12–14, pp. 12–14. https://sffsymposium.engr.utexas.edu/Manuscripts/2013/2013-01-Pal.pdf
Paul, R. , Anand, S. , and Gerner, F. , 2014, “ Effect of Thermal Deformation on Part Errors in Metal Powder Based Additive Manufacturing Processes,” ASME J. Manuf. Sci. Eng., 136(3), p. 031009. [CrossRef]
Jiang, C.-P. , Huang, Y.-M. , and Liu, C.-H. , 2006, “ Dynamic Finite Element Analysis of Photopolymerization in Stereolithography,” Rapid Prototyping J., 12(3), pp. 173–180. [CrossRef]
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]
Barnfather, J. , Goodfellow, M. , and Abram, T. , 2016, “ A Performance Evaluation Methodology for Robotic Machine Tools Used in Large Volume Manufacturing,” Rob. Comput.-Integr. Manuf., 37, pp. 49–56. [CrossRef]
Cooke, A. , and Soons, J. , 2010, “ Variability in the Geometric Accuracy of Additively Manufactured Test Parts,” 21st Annual International Solid Freeform Fabrication Symposium (SFF), Austin, TX, Aug. 9–11, pp. 1–12. https://sffsymposium.engr.utexas.edu/Manuscripts/2010/2010-01-Cooke.pdf
Moylan, S. , Cooke, A. , Jurrens, K. , Slotwinski, J. , and Donmez, M. A. , 2012, “A Review of Test Artifacts for Additive Manufacturing,” National Institute of Standards and Technology (NIST), Gaithersburg, MD, Report No. NISTIR-7858. http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=910625
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]
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]
Schnabel, R. , Wahl, R. , and Klein, R. , 2007, “ Efficient RANSAC for Point‐Cloud Shape Detection,” Comput. Graphics Forum, 26(2), pp. 214–226. [CrossRef]
Liu, G. , Wong, Y. , Zhang, Y. , and Loh, H. , 2003, “ Modelling Cloud Data for Prototype Manufacturing,” J. Mater. Process. Technol., 138(1), pp. 53–57. [CrossRef]
Sun, W. , Bradley, C. , Zhang, Y. , and Loh, H. T. , 2001, “ Cloud Data Modelling Employing a Unified, Non-Redundant Triangular Mesh,” Comput.-Aided Des., 33(2), pp. 183–193. [CrossRef]
Fabio, R. , 2003, “ From Point Cloud to Surface: The Modeling and Visualization Problem,” Int. Arch. Photogram., Remote Sensing Spatial Inf. Sci., 34(5), p. W10. http://www.isprs.org/proceedings/XXXIV/5-W10/papers/remondin.pdf
Mémoli, F. , and Sapiro, G. , 2005, “ A Theoretical and Computational Framework for Isometry Invariant Recognition of Point Cloud Data,” Found. Comput. Math., 5(3), pp. 313–347. [CrossRef]
Aboutaleb, A. M. , Tschopp, M. A. , Rao, P. K. , and Bian, L. , 2017, “ Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing,” ASME J. Manuf. Sci. Eng., 139(10), p. 101001. [CrossRef]
Tootooni, M. S. , Dsouza, A. , Donovan, R. , Rao, P. K. , Kong, Z. J. , and Borgesen, P. , 2017, “ Classifying the Dimensional Variation in Additive Manufactured Parts From Laser-Scanned Three-Dimensional Point Cloud Data Using Machine Learning Approaches,” ASME J. Manuf. Sci. Eng., 139(9), p. 091005. [CrossRef]
Tootooni, M. S. , Dsouza, A. , Donovan, R. , Rao, P. K. , Kong, Z. J. , and Borgesen, P. , 2017, “Assessing the Geometric Integrity of Additive Manufactured Parts From Point Cloud Data Using Spectral Graph Theoretic Sparse Representation-Based Classification,” ASME Paper No. MSEC2017-2794.
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. https://search.proquest.com/openview/6001a6e9091c3b0366ac90fe225b38f5/1?pq-origsite=gscholar&cbl=18750&diss=y
Zhang, Y. , and Chou, K. , 2008, “ A Parametric Study of Part Distortions in Fused Deposition Modelling Using Three-Dimensional Finite Element Analysis,” Proc. Inst. Mech. Eng., Part B, 222(8), pp. 959–968. [CrossRef]
Villmann, T. , Schleif, F.-M. , Kaden, M. , and Lange, M. , 2014, Advances in Self-Organizing Maps and Learning Vector Quantization, Springer, Cham, Switzerland.
Marandi, R. J. , and Keramati, A. , 2014, “ Webpage Clustering–Taking the Zero Step: A Case Study of an Iranian Website,” J. Web Eng., 13(3–4), pp. 333–360. https://www.researchgate.net/publication/287423858_Webpage_clustering_-_Taking_the_zero_step_A_case_study_of_an_iranian_website
Patel, R. K. , and Giri, V. , 2016, “ Analysis and Interpretation of Bearing Vibration Data Using Principal Component Analysis and Self-Organizing Map,” Int. J. Adv. Des. Manuf. Technol., 9(1), pp. 111–117. https://www.researchgate.net/publication/309429020_Analysis_and_Interpretation_of_Bearing_Vibration_Data_Using_Principal_Component_Analysis_and_Self-_Organizing_Map
Kanungo, T. , Mount, D. M. , Netanyahu, N. S. , Piatko, C. D. , Silverman, R. , and Wu, A. Y. , 2002, “ An Efficient k-Means Clustering Algorithm: Analysis and Implementation,” IEEE Trans. Pattern Anal. Mach. Intell., 24(7), pp. 881–892. [CrossRef]
Tootooni, M. S. , 2016, “Sensor Based Monitoring of Multidimensional Complex Systems Using Spectral Graph Theory,” Ph.D. dissertation, Binghamton University, Binghamton, NY. https://search.proquest.com/openview/c301704434a6b02b48ba188d3d64bdd6/1?pq-origsite=gscholar&cbl=18750&diss=y


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Fig. 1

The design part used to measure GD&T. Subfigure (a) shows the 3D view of the fabricated test part using FFF process. Subfigure (b) shows the faces used to measure flatness (), circularity (), and cylindricity (); and (c) shows the planes used to measure the thickness—three thickness measurements are taken on each plane [13]. Different colors represent various planes and the point cloud data.

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Fig. 2

The eight points used for alignment of the scan points with the CAD model [3639]

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Fig. 3

Fracture strength of ASTM 638D-Type V samples under different infill percentages. The error bars are ±1 standard deviation (eight samples at each infill percentage level).

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Fig. 4

Flooded contour plots of the benchmark part used in FFF experiments detailed in Sec. 3.1. 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. (a)–(d) represent different parts, printed under 70%, 80%, 90%, and 100% infill percentages at 230 °C, respectively. The scale bar is in mm (the darker the worse) [14].

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Fig. 5

Micrograph showing the effect of infill percentage on the internal morphology of the circle square diamond part at 230 °C extruder temperature. 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 geometric integrity, since there is no vacant space for stress relief.

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Fig. 6

Comparing the flatness and thickness of GD&T characteristics using various set of process conditions (infill (If), extruder temperature (te)). The circle size represents the ratio between thickness and flatness.

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Fig. 7

Clustering point cloud data according to the direction and magnitude of geometric deviations using SOMs to a 4 × 4 quadrilateral space membership map

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Fig. 8

SOM based deviation clustering method applied to different FFF process conditions: (left) te = 225 °C, If = 100%; (right) te = 230 °C, If = 80%

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Fig. 9

(a) Comparing magnitudes of various types of deviations for two distinct parts in the direction in their corresponding x-direction and y-direction, where circles and squares illustrate the magnitude of deviation in the z-direction (te= 225 °C, If = 100%) and (te=230 °C,If= 100%), respectively. (b) Comparing magnitude of various types of deviations for two distinct parts in the z-direction in their corresponding x-direction and y-direction, where triangles and diamonds illustrate the magnitude of deviation in the z-direction for (te= 225 °C, If= 80%) and (te= 230 °C, If = 80%), respectively.

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Fig. 10

Comparing the geometric quality of printed parts using FFF where two levels of (te = 225 °C and 230 °C) and three levels of (If = 80, 90, and 100%) are selected

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Fig. 11

Selecting the top k clusters based on estimated minimum magnitude of deviation

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Fig. 12

Identifying the new scanning areas based on location of point cloud data in top four clusters where (a) illustrates thin-exponentially shaped scanning pattern for designed part using point cloud data based on deviations from cluster 22, (b) demonstrates thin-circular shaped scanning pattern for designed part using point cloud data formed due to deviations of clusters 16 and 21, and (c) shows thin-cubic shaped scanning pattern for designed part using point cloud data depending on deviations of cluster 11 (the scanning pattern demonstrated from top and right view for (a) and (b) and bottom and right view for (c), respectively)

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Fig. 13

Comparing the geometric quality of printed parts using FFF where two levels of (te=225 °C and230 °C) and three levels of (If= 80, 90, and 100%) are selected using K-means clustering method




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