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research-article

Surrogate Model Based Control Considering Uncertainty for Composite Fuselage Assembly

[+] Author and Article Information
Xiaowei Yue

ASME member, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332
xwy@gatech.edu

Yuchen Wen

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

Jeff Hunt

APS Fellow, OSA Fellow, The Boeing Company, 900 N Sepulveda Blvd, El Segundo, CA 90245
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
jianjun.shi@isye.gatech.edu

1Corresponding author.

ASME doi:10.1115/1.4038510 History: Received March 22, 2017; Revised September 19, 2017

Abstract

Shape control of composite parts is vital for large-scale production and integration of composite materials in the aerospace industry. Current industry practice of shape control uses passive manual metrology. This has three major limitations: (i) low efficiency: it requires multiple trials to achieve a desired shape during the assembly leading to longer assembly times; (ii) non-optimal: it makes it 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 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; (iii) conducting multivariable optimization to determine the optimal actions of actuators. We show that the surrogate model considering uncertainties achieves satisfactory prediction performance and that the automated optimal shape control system can significantly reduce the time with improved dimensional quality.

Copyright (c) 2017 by ASME
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