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Research Papers

Surrogate Model-Based Control Considering Uncertainties for Composite Fuselage Assembly

[+] Author and Article Information
Xiaowei Yue

Mem. ASME
H. Milton Stewart School of Industrial and
Systems Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: xwy@gatech.edu

Yuchen Wen

H. Milton Stewart School of Industrial and
Systems Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: ycwen@gatech.edu

Jeffrey H. Hunt

The Boeing Company,
900 N Sepulveda Boulevard,
El Segundo, CA 90245
e-mail: jeffrey.h.hunt@boeing.com

Jianjun Shi

ASME Fellow
H. Milton Stewart School of Industrial and
Systems Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: jianjun.shi@isye.gatech.edu

1Corresponding author.

Manuscript received March 22, 2017; final manuscript received September 19, 2017; published online February 15, 2018. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 140(4), 041017 (Feb 15, 2018) (13 pages) Paper No: MANU-17-1157; doi: 10.1115/1.4038510 History: Received March 22, 2017; Revised September 19, 2017

Shape control of composite parts is vital for large-scale production and integration of composite materials in the aerospace industry. The current industry practice of shape control uses passive manual metrology. This has three major limitations: (i) low efficiency: it requires multiple trials and a longer time to achieve the desired shape during the assembly process; (ii) nonoptimal: it is challenging to reach optimal deviation reduction; and (iii) experience-dependent: highly skilled engineers are required during the assembly process. This paper describes an automated shape control system that can adjust composite parts to an optimal configuration in a manner that is highly effective and efficient. The objective is accomplished by (i) building a finite element analysis (FEA) platform, validated by experimental data; (ii) developing a surrogate model with consideration of actuator uncertainty, part uncertainty, modeling uncertainty, and unquantified uncertainty to achieve predictive performance and embedding the model into a feed-forward control algorithm; and (iii) conducting multivariable optimization to determine the optimal actions of actuators. We show that the surrogate model considering uncertainties (SMU) achieves satisfactory prediction performance and that the automated optimal shape control system can significantly reduce the assembly time with improved dimensional quality.

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References

Chawla, K. K. , 2012, Composite Materials: Science and Engineering, Springer Science & Business Media, New York, Chap. 5.
Gates, D. , 2007, “Boeing Finds 787 Pieces Are Not Quite a Perfect Fit,” Seattle Times Aerospace Report, Seattle Times, Seattle, WA, accessed Nov. 29, 2017, http://www.seattletimes.com/business/boeing-finds-787-pieces-arent-quite-a-perfect-fit/
Jin, J. , and Shi, J. , 1999, “State Space Modeling of Sheet Metal Assembly for Dimensional Control,” ASME J. Manuf. Sci. Eng., 121(4), pp. 756–762. [CrossRef]
Shi, J. , 2006, Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes, CRC Press, Boca Raton, FL. [CrossRef]
Djurdjanovic, D. , and Ni, J. , 2001, “Linear State Space Modeling of Dimensional Machining Errors,” NAMRI/SME Trans., 29, pp. 541–548. https://faculty.engr.utexas.edu/sites/default/files/limes/files/j42.pdf
Djurdjanovic, D. , and Ni, J. , 2003, “Dimensional Errors of Fixtures, Locating and Measurement Datum Features in the Stream of Variation Modeling in Machining,” ASME J. Manuf. Sci. Eng., 125(2), pp. 716–730. [CrossRef]
Camelio, J. , Hu, S. , and Ceglarek, D. , 2004, “Modeling Variation Propagation of Multi-Station Assembly Systems With Compliant Parts,” ASME J. Mech. Des., 125(4), pp. 673–681. [CrossRef]
Liu, S. , and Hu, S. , 1997, “Variation Simulation for Deformable Sheet Metal Assemblies Using Finite Element Methods,” ASME J. Manuf. Sci. Eng., 119(3), pp. 368–374. [CrossRef]
Zhang, T. , and Shi, J. , 2016, “Stream of Variation Modeling and Analysis for Compliant Composite Part Assembly—Part I: Single-Station Processes,” ASME J. Manuf. Sci. Eng., 138(12), p. 121003. [CrossRef]
Zhang, T. , and Shi, J. , 2016, “Stream of Variation Modeling and Analysis for Compliant Composite Part Assembly—Part II: Multistation Processes,” ASME J. Manuf. Sci. Eng., 138(12), p. 121004. [CrossRef]
Li, Z. , and Zhou, S. , 2005, “Robust Method of Multiple Variation Sources Identification in Manufacturing Processes for Quality Improvement,” ASME J. Manuf. Sci. Eng., 128(1), pp. 326–336. [CrossRef]
Zhang, B. , and Ni, J. , 2003, “Adaptive Product, Process and Tooling Design Strategy for Optimal Dimensional Quality of Automotive Body Assemblies,” ASME J. Manuf. Sci. Eng., 125(4), pp. 835–843. [CrossRef]
Abad, A. G. , Paynabar, K. , and Jin, J. , 2011, “Modeling and Analysis of Operator Effects on Process Quality and Throughput in Mixed Model Assembly Systems,” ASME J. Manuf. Sci. Eng., 133(2), p. 021016. [CrossRef]
Camelio, J. A. , Hu, S. , and Marin, S. P. , 2004, “Compliant Assembly Variation Analysis Using Component Geometric Covariance,” ASME J. Manuf. Sci. Eng., 126(2), pp. 355–360. [CrossRef]
Zhong, J. , Liu, J. A. , and Shi, J. J. , 2010, “Predictive Control Considering Model Uncertainty for Variation Reduction in Multistage Assembly Processes,” IEEE Trans. Autom. Sci. Eng., 7(4), pp. 724–735. [CrossRef]
Djurdjanovic, D. , and Ni, J. , 2007, “Online Stochastic Control of Dimensional Quality in Multistation Manufacturing Systems,” J. Eng. Manuf., 221(5), pp. 865–880. [CrossRef]
Åström, K. J. , and Wittenmark, B. , 2013, Adaptive Control, Prentice Hall, Upper Saddle River, NJ.
Basar, T. , and Bernhard, P. , 1995, H-Infinity Optimal Control and Related Minimax Design Problems: A Dynamic Game Approach, 2nd ed., Birkhäuser, Boston, MA.
Tanaka, K. , and Sugeno, M. , 1992, “Stability Analysis and Design of Fuzzy Control Systems,” Fuzzy Sets Syst., 45(2), pp. 135–156. [CrossRef]
Zhou, K. , Doyle, J. C. , and Glover, K. , 1996, Robust and Optimal Control, Prentice Hall, Upper Saddle River, NJ.
Hansen, L. P. , and Sargent, T. J. , 2001, “Robust Control and Model Uncertainty,” Am. Econ. Rev., 91(2), pp. 60–66. [CrossRef]
Uusitalo, L. , Lehikoinen, A. , Helle, I. , and Myrberg, K. , 2015, “An Overview of Methods to Evaluate Uncertainty of Deterministic Models in Decision Support,” Environ. Modell. Software, 63, pp. 24–31. [CrossRef]
Draper, D. , 1995, “Assessment and Propagation of Model Uncertainty,” J. R. Stat. Soc. Ser. B, 57(1), pp. 45–97. https://classes.soe.ucsc.edu/ams206/Winter11/draper-1995-model-uncertainty.pdf
Ayyub, B. M. , and Klir, G. J. , 2006, Uncertainty Modeling and Analysis in Engineering and the Sciences, CRC Press, Boca Raton, FL. [CrossRef]
Jones, R. M. , 1998, Mechanics of Composite Materials, CRC Press, Boca Raton, FL.
Neter, J. , Kutner, M. H. , Nachtsheim, C. J. , and Wasserman, W. , 1996, Applied Linear Statistical Models, Vol. 4, McGraw-Hill, New York.
Santner, T. J. , Williams, B. J. , and Notz, W. I. , 2003, The Design and Analysis of Computer Experiments, Springer Science & Business Media, New York. [CrossRef]
Ankenman, B. , Nelson, B. L. , and Staum, J. , 2010, “Stochastic Kriging for Simulation Metamodeling,” Oper. Res., 58(2), pp. 371–382. [CrossRef]
Stein, M. L. , 1999, Interpolation of Spatial Data: Some Theory for Kriging, Springer Science & Business Media, New York. [CrossRef]
United States Department of Transportation, Federal Aviation Administration, 2012, “A. M. T. Handbook-Airframe,” United States Department of Transportation, Federal Aviation Administration, Oklahoma City, OK, accessed Nov. 29, 2017, https://www.faa.gov/regulations_policies/handbooks_manuals/aircraft/amt_airframe_handbook/
Izquierdo, L. E. , Shi, J. , Hu, S. J. , and Wampler, C. W. , 2007, “Feedforward Control of Multistage Assembly Processes Using Programmable Tooling,” Trans. NAMRI/SME, 35, pp. 295–302. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=A3D79055C092D00301A49EB9C1659A8E?doi=10.1.1.157.857&rep=rep1&type=pdf
Boyd, S. , and Vandenberghe, L. , 2004, Convex Optimization, Cambridge University Press, New York. [CrossRef]
ANSYS, 2013, “ANSYS Composite PrepPost User's Guide,” ANSYS, Inc., Canonsburg, PA, accessed Nov. 29, 2017, http://www.academia.edu/25099111/ANSYS_Composite_PrepPost_Users_Guide
Wang, Y. , Yue, X. , Tuo, R. , Hunt, J. H. , and Shi, J. , 2017, “Effective Model Calibration Via Sensible Variable Identification and Adjustment, With Application to Composite Fuselage Simulation,” (submitted).

Figures

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Fig. 1

Schematic diagram for shape adjustment: (a) layout of ten actuators, (b) sketch map of the shape adjustment, and (c) key points

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Fig. 2

Overview of the proposed methodology

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Fig. 7

Prediction errors of the four methods based on the training dataset

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Fig. 8

Prediction errors of the four methods based on the testing dataset

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Fig. 3

Feed-forward AOSC algorithm

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Fig. 4

Comparison between FEA simulation model and real testing experiment setup: (a) FEA simulation model and (b) real testing experiment setup

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Fig. 5

The dimensional deviations under different actuator' forces in the FEA simulation and the physical experiment

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Fig. 6

Examples of datasets generated with a designed experiment

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Fig. 9

Deviations after control based on the four models

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Fig. 10

Sensitivity analysis for fuselage variability and maximum actuators' forces in the AOSC system

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Fig. 11

Maximum stress under different magnitudes of actuators' forces. Note: the upper and lower dashed line represents the limit of stress.

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