Research Papers

Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

[+] Author and Article Information
Amir M. Aboutaleb

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

Mark A. Tschopp

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

Prahalad K. Rao

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

Linkan Bian

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

1Corresponding author.

Manuscript received December 17, 2016; final manuscript received July 10, 2017; published online August 24, 2017. Assoc. Editor: Moneer Helu.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 139(10), 101001 (Aug 24, 2017) (13 pages) Paper No: MANU-16-1656; doi: 10.1115/1.4037319 History: Received December 17, 2016; Revised July 10, 2017

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

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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]
Tootooni, M. S. , Dsouza, A. , Donovan, R. , Rao, P. , Kong, Z. , 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.
Tootooni, M. S. , Dsouza, A. , Donovan, R. , Rao, P. , Kong, Z. , 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. , 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
Dsouza, A. , 2016, “ Experimental Evolutionary Optimization of Geometric Integrity in Fused Filament Fabrication (FFF) Additive Manufacturing (AM) Process,” Master's thesis, Binghamton University, Binghamton, NY. https://search.proquest.com/openview/6001a6e9091c3b0366ac90fe225b38f5/1?pq-origsite=gscholar&cbl=18750&diss=y
Badiru, A. B. , Valencia, V. V. , and Liu, D. , 2017, Additive Manufacturing Handbook: Product Development for the Defense Industry, CRC Press, Boca Raton, FL.
Vasinonta, A. , and Beuth, J. , 2000, “ Process Maps for Controlling Residual Stress and Melt Pool Size in Laser-Based SFF Processes,” Solid Freeform Fabrication Symposium, Austin, TX, Aug. 7–9, pp. 200–208. https://sffsymposium.engr.utexas.edu/Manuscripts/2000/2000-25-Vasinonta.pdf
Bontha, S. , Klingbeil, N. W. , Kobryn, P. A. , and Fraser, H. L. , 2009, “ Effects of Process Variables and Size-Scale on Solidification Microstructure in Beam-Based Fabrication of Bulky 3D Structures,” Mater. Sci. Eng. A, 513–514, pp. 311–318. [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]
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]
Huang, Q. , Nouri, H. , Xu, K. , Chen, Y. , Sosina, S. , and Dasgupta, T. , 2014, “ Statistical Predictive Modeling and Compensation of Geometric Deviations of Three-Dimensional Printed Products,” ASME J. Manuf. Sci. Eng., 136(6), p. 061008. [CrossRef]
Aboutaleb, A. M. , Bian, L. , Elwany, A. , Shamsaei, N. , Thompson, S. M. , and Tapia, G. , 2016, “ Accelerated Process Optimization for Laser-Based Additive Manufacturing by Leveraging Similar Prior Studies,” IIE Trans., 49(1), pp. 1–14. [CrossRef]
Bochmann, L. , Bayley, C. , Helu, M. , Transchel, R. , Wegener, K. , and Dornfeld, D. , 2015, “ Understanding Error Generation in Fused Deposition Modeling,” Surf. Topogr.: Metrol. Prop., 3(1), p. 014002. [CrossRef]
Mahesh, M. , Wong, Y. S. , Fuh, J. Y. H. , and Loh, H. T. , 2004, “ Benchmarking for Comparative Evaluation of RP Systems and Processes,” Rapid Prototyping J., 10(2), pp. 123–135. [CrossRef]
El-Katatny, I. , Masood, S. H. , and Morsi, Y. S. , 2010, “ Error Analysis of FDM Fabricated Medical Replicas,” Rapid Prototyping J., 16(1), pp. 36–43. [CrossRef]
Weheba, G. , and Sanchez-Marsa, A. , 2006, “ Using Response Surface Methodology to Optimize the Stereolithography Process,” Rapid Prototyping J., 12(2), pp. 72–77. [CrossRef]
Rao, P. K. , Kong, Z. , Duty, C. E. , Smith, R. J. , Kunc, V. , and Love, L. J. , 2015, “ 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]
Fathi, A. , and Mozaffari, A. , 2014, “ Vector Optimization of Laser Solid Freeform Fabrication System Using a Hierarchical Mutable Smart Bee-Fuzzy Inference System and Hybrid NSGA-II/Self-Organizing Map,” J. Intell. Manuf., 25(4), pp. 775–795. [CrossRef]
Deshpande, S. , Watson, L. T. , and Canfield, R. A. , 2013, “ Pareto Front Approximation Using a Hybrid Approach,” Procedia Comput. Sci., 18, pp. 521–530. [CrossRef]
Eichfelder, G. , 2008, Adaptive Scalarization Methods in Multiobjective Optimization, Springer, Berlin. [CrossRef]
Abraham, A. , and Jain, L. , 2005, “ Evolutionary Multiobjective Optimization,” Evolutionary Multiobjective Optimization, Springer-Verlag, London, pp. 1–6.
Kunath, S. , Marchyk, N. , Haupt, K. , and Feller, K.-H. , 2013, “ Multi-Objective Optimization and Design of Experiments as Tools to Tailor Molecularly Imprinted Polymers Specific for Glucuronic Acid,” Talanta, 105, pp. 211–218. [CrossRef] [PubMed]
Moylan, S. , Cooke, A. , Jurrens, K. , Slotwinski, J. , Alkan Donmez, M. , Bryson, J. E. , and Gallagher, P. D. , 2012, “ A Review of Test Artifacts for Additive Manufacturing,” National Institute of Standards and Technology, Gaithersburg, MD, Report No. NISTIR 7858. http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=910625
ASME, 2005, “ Methods for Performance Evaluation of Computer Numerically Controlled Machining Centers,” American Society of Mechanical Engineers, New York, Standard No. ASME B5.54. https://www.asme.org/products/codes-standards/b554-2005-methods-performance-evaluation-computer
AIA/NAS, 1969, “ NAS 979 Uniform Cutting Tests—NAS Series Metal Cutting Equipment Specifications,” Aerospace Industries Association of America Inc., Arlington, VA, Standard No. AIA/NAS-NAS979. http://standards.globalspec.com/std/1603751/aia-nas-nas979
Moylan, S. , Slotwinski, J. , Cooke, A. , Jurrens, K. , and Alkan Donmez, M. , 2014, “ An Additive Manufacturing Test Artifact,” J. Res. Natl. Inst. Stand. Technol., 119, pp. 429–459. [CrossRef] [PubMed]
ASME, 2009, “ Dimensioning and Tolerancing—Engineering Drawing and Related Documentation Practices,” American Society of Mechanical Engineers, New York, Standard No. ASME Y14.5.
Aboutaleb, A. M. , Bian, L. , Shamsaei, N. , Thompson, S. M. , and Rao, P. K. , 2016, “ Multi-Objective Process Optimization of Additive Manufacturing: A Case Study on Geometry Accuracy Optimization,” Annual International Solid Freeform Fabrication Symposium, Austin, TX, Aug. 8–10, pp. 656–669. https://par.nsf.gov/biblio/10023972
Aboutaleb, A. M. , Bian, L. , Shamsaei, N. , and Thompson, S. M. , 2016, “ Systematic Optimization of Laser-Based Additive Manufacturing for Multiple Mechanical Properties,” IEEE International Conference on Automation Science and Engineering (COASE), Fort Worth, TX, Aug. 21–25, pp. 780–785.
Dasgupta, T. , 2007, “ Robust Parameter Design for Automatically Controlled Systems and Nanostructure Synthesis,” Ph.D. dissertation, Georgia Institute of Technology, Atlanta, GA. https://search.proquest.com/openview/6986d3882844b6d32ed82ae2e1424867/1?pq-origsite=gscholar&cbl=18750&diss=y
Knowles, J. , 2006, “ ParEGO: A Hybrid Algorithm With On-Line Landscape Approximation for Expensive Multiobjective Optimization Problems,” IEEE Trans. Evol. Comput., 10(1), pp. 50–66. [CrossRef]
Okabe, T. , Jin, Y. , Olhofer, M. , and Sendhoff, B. , 2004, “ On Test Functions for Evolutionary Multi-Objective Optimization,” International Conference on Parallel Problem Solving From Nature (PPSN), Edinburgh, UK, Sept. 17–21, pp. 792–802. https://doi.org/10.1007/978-3-540-30217-9_80
Kim, I. Y. , and de Weck, O. L. , 2005, “ Adaptive Weighted-Sum Method for Bi-Objective Optimization: Pareto Front Generation,” Struct. Multidiscip. Optim., 29(2), pp. 149–158. [CrossRef]
Huband, S. , Hingston, P. , Barone, L. , and While, L. , 2006, “ A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit,” IEEE Trans. Evol. Comput., 10(5), pp. 477–506. [CrossRef]
Van Veldhuizen, D. A. , and Lamont, G. B. , 1998, “ Multiobjective Evolutionary Algorithm Research: A History and Analysis,” Air Force Institute of Technology, Greene, OH, Technical Report No. TR-98-03. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=
Ryu, J.-H. , Kim, S. , and Wan, H. , 2009, “ Pareto Front Approximation With Adaptive Weighted Sum Method in Multiobjective Simulation Optimization,” Winter Simulation Conference (WSC), Austin, TX, Dec. 13–16, pp. 623–633.


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Fig. 1

Flooded contour plots (deviation maps) of the benchmark part used in FFF experiments detailed in Sec. 3.1. The material is acrylonitrile butadiene styrene polymer. The first row (1) shows the top views, and the second row (2) contains the bottom views of the parts. (a)–(d) Different parts, printed under 70%, 80%, 90%, and 100% infill percentages at 230 °C, respectively. The reference scale is in millimeter [25].

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Fig. 2

(a) Contour plot of part concentricity versus infill percentage (If) and extruder temperature (te). (b) Contour plot of flatness versus infill percentage (If) and extruder temperature (te).

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Fig. 3

Design of the circle–square–diamond part—a simplified embodiment of the NAS 979 standard test artifact for testing accuracy of machining centers [25]. The dimensions are in millimeters. (a) and (b) Front and top views of the part, respectively, and (c) an isometric projection of the part [25].

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Fig. 4

The areas used to measure GD&T form the design part. (a) The faces used to measure flatness (), circularity (), and cylindricity (); (b) the planes used to measure the thickness—three thickness measurements are taken on each plane [25].

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Fig. 5

The eight points used for alignment of the scan points with the CAD model [25]

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Fig. 6

The representation of the data scatter plot matrix to illustrate both positive and negative correlations among pairs of part geometric characteristics (i.e., flatness, circularity, cylindricity, concentricity, and thickness). The slope of lines illustrates the Pearson correlation coefficient (ρ) for pairs of GD&T characteristics.

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Fig. 7

Schematic illustration of design space, objective space, nondominated design points, Pareto points, and Pareto front

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Fig. 8

Leveraging the information from prior data to accelerate solving subsequent subproblems

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Fig. 10

Schematic illustration of HV as the measure of the contribution of Pareto points. The rectangle represents ΔHV, i.e., the contribution of a new Pareto point in terms of the improvement in HV.

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Fig. 11

A schematic diagram of the FFF process [2,3]

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Fig. 12

Demonstrating the Pareto points and conducted experiments for the case study

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Fig. 13

Case A: Discretization of objective space for test problem with nonconvex Pareto front and well-distributed objective space

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Fig. 14

Case B: Discretization of objective space for test problem with nonconvex Pareto front and congested objective space

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Fig. 15

Case C: Discretization of objective space for test problem with increased number of process parameters

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Fig. 16

Case A: Comparing estimated Pareto front resulted by m-APO and full factorial DOE with the true Pareto front (test problem with nonconvex Pareto front and well-distributed objective space)

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Fig. 17

Case B: Comparing estimated Pareto front resulted by m-APO and full factorial DOE with the true Pareto front (test problem with nonconvex Pareto front and congested objective space)

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Fig. 18

Case C: Comparing estimated Pareto front resulted by m-APO and full factorial DOE with the true Pareto front (test problem with increased number of process parameters)



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