Surrogate model–based optimal feed-forward control for dimensional-variation reduction in composite parts' assembly processes

ABSTRACT

Dimension control and variation reduction are vital for composite parts' assembly processes. Due to the nonlinear properties of composites, physics-based models cannot accurately and efficiently approximate the assembly processes. In addition, conventional robust parameter design (RPD) and statistical process control (SPC) cannot actively compensate for dimensional errors or prevent defects. This article proposes a surrogate model–based optimal feed-forward control strategy for dimensional-variation reduction and defect prevention in the assembly of composite parts. The objective is accomplished by (i) developing a grouped Latin hypercube sampling approach tailored to the problem; (ii) adopting a universal Kriging model for dimensional prediction and then embedding the model into an optimal feed-forward control algorithm; and (iii) conducting a multiobjective optimization to determine the control actions. A case study reveals that the developed methodology can effectively reduce the mean and standard deviation of dimensional deviations for the assembly of composite parts.

KEYWORDS:

• composites assembly processes
• feed-forward control
• Latin hypercube sampling
• surrogate model
• variation reduction

About the authors

Mr. Yue is a PhD candidate in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He is a member of the American Society for Quality. His email address is [email protected].

Dr. Shi is the Carolyn J. Stewart Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He is a senior member of the American Society for Quality. His email address is [email protected].