PhD Defense by Yuchen Wen

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  • Date/Time:
    • Monday December 3, 2018
      8:30 am - 10:30 am
  • Location: ISyE Groseclose 303
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Summary Sentence: Stream of Variation and Shape Control for Composite Fuselages Assembly

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Thesis Title: Stream of Variation and Shape Control for Composite Fuselages Assembly 

Advisor: Dr. Jianjun (Jan) Shi


Committee members (order by last name):

Dr. Judy Jin (University of Michigan)

Dr. Kamran Paynabar  

Dr. Ben Wang 

Dr. Chuck Zhang 


Date and Time: Monday, Dec 3, 2018, 8:30 AM to 10:30 AM


Location: ISyE Groseclose 303



Composite parts have been increasingly used in aircraft industry because of their high strength-to-weight ratio and stiffness-to-weight ratio. Due to the diversity of suppliers and fabrication process variation of composite parts, dimensional variability of composite fuselages inevitably exists. One of the critical challenges to reduce the dimensional variability of the assembly process is the complex property of composite materials. The traditional physical models applied to metal materials cannot be directly applied here. It is significant to develop systematic methodologies to realize dimensional analysis, variation reduction, and optimal shape adjustment for the composite fuselages assembly process.

Based on these motivations, the dissertation focuses on developing systematic methodologies for effective system modeling, quality control and variability reduction in the composite fuselages assembly process, which contributes to the area of System Informatics and Control (SIAC). These advanced methodologies enable a better understanding of the composite fuselage structure with actuator applied, a more accurate handling of the fuselage shape control system, and a more precise way to analyze the residual stress during the fuselage assembly process.

This dissertation is organized as follows. In Chapter 1, a new shape control system with ten actuators that is able to conduct dimensional shape adjustment before the airplane fuselage assembly process is introduced. In Chapter 2, a feasibility study is conducted to evaluate the proposed shape control concept.  In order to do the feasibility analysis, an accurate finite element model is developed to mimic the fabrication of composite fuselage, including the detailed materials setting, ply design, geometry and fixture structures. The finite element model is validated and calibrated based on physical experimental data with a real fuselage on the production floor. The results show that the single-plane with ten actuators scheme is feasible for the shape control, and the actuators do not damage the fuselage.

In Chapter 3, a surrogate model considering four types of uncertainties (actuator uncertainty, part uncertainty, modeling uncertainty, and unquantified uncertainty) has been developed to achieve automatic optimal shape control of the system proposed in Chapter 2. An MLE estimation algorithm has been used for parameter estimation and response prediction. Afterward, the surrogated model considering uncertainties is embedded into a feedforward control algorithm, which is achieved by conducting multivariable optimization to minimize the weighted summation of dimensional deviations of the response to the target. We show that the surrogate model considering uncertainties achieves satisfactory prediction performance and the automated optimal shape control system can significantly reduce the assembly time with improved dimensional quality.

In Chapter 4, two active learning algorithms are proposed for Gaussian process considering uncertainties, which are variance-based weighted active learning algorithm and D-optimal weighted active learning algorithm. These active learning algorithms effectively reduce the number of samples needed to get accurate statistical models for industrial systems that have numerous uncertainties, such as input uncertainties, measurement errors, modeling uncertainties and uncertainties from system parameters. The proposed algorithms investigate stochastic Kriging model and surrogate model considering uncertainties, with information measure of variance-based information and Fisher information. The algorithms have been applied to improving the predictive modeling for automatic shape control of composite fuselage. They can also be applied in other active learning scenarios for predictive models with multiple uncertainties.

From Chapter 2 to Chapter 4, we have focused on optimal shape control system for single fuselage. After the two fuselages are adjusted to target shape, they will be assembled together, and the actuator forces are released afterwards. The release of actuator forces results in the springback of two fuselages and the occurrence of residual stresses. In Chapter 5, we investigate the process of fuselages assembly via the proposed control system and develop a FEA platform to show the dimensional change and stress change during and after the assembly process.  Instead of reversing the actuator forces, we use dynamic forces to simulate the springback of the fuselages after releasing the actuators. The proposed approach is more accurate compared to the traditional simulation approach, and the results show that the assembly process with new shape control system is applicable since the residual stresses are lower than the failure threshold.

In Chapter 6, we discuss the potentials to extend the full-full fuselage assembly to the half-half fuselage assembly and the need of reducing dimensional variations during fuselage transportation.  

In summary, this thesis fuses the knowledge of statistics, mechanical engineering and material science to get better understanding and improvement of the composite fuselage assembly process. The methodologies and simulations developed in this thesis have potential to be applied to other advanced manufacturing systems.            


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Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 29, 2018 - 10:40am
  • Last Updated: Nov 29, 2018 - 10:40am