SPAC: Sparse sensor placement-based adaptive control for high precision fuselage assembly

Abstract

Optimal shape control is important in fuselage assembly processes. To achieve high precision assembly, shape adjustment is necessary for fuselages with initial shape deviations. The state-of-the-art methods accomplish this goal by using actuators whose forces are derived from a model based on the mechanical properties of the designed fuselage. This has a significant limitation: they do not consider the model mismatch due to mechanical property changes induced by the shape deviation of an individual incoming fuselage. The model mismatch will result in control performance deterioration. To improve the performance, the shape control model needs to be updated based on the online feedback information from the fuselage shape adjustment. However, due to the large size of the fuselage surface, highly accurate inline measurements are expensive or even infeasible to obtain in practice. To resolve those issues, this article proposes a Sparse sensor Placement-based Adaptive Control methodology. In this method, the model is updated based on the sparse sensor measurement of the response signal. The reconstruction performance under a minor model mismatch is quantified theoretically. Its performance has been evaluated based on real data of a half-to-half fuselage assembly process, and the proposed method improves the control performance with acceptable sensing effort.

Keywords:

• Adaptive control
• composite fuselage assembly
• sparse sensor placement
• quality improvement

Data and code availability statement

The code is available via the link: https://github.com/Shancong-Mou/SPAC.

Additional information

Funding

The work is supported by the Strategic University Partnership between the Boeing Company and the Georgia Institute of Technology (Funder ID: 10.13039/100000003), and partially supported by the National Science Foundation CMMI-2035038 and Grainger Frontiers of Engineering Grant Award from the U.S. National Academy of Engineering (NAE).

Notes on contributors

Shancong Mou

Shancong Mou received a BS degree in energy and power engineering from Xi’an Jiaotong University, Xi’an, China, in 2017. He is currently pursuing a PhD degree with the School of Industrial and Systems Engineering, Georgia Tech, Atlanta, GA, USA. His research interests include data analytics for monitoring, control, and diagnostics of complex engineering systems. Mr. Mou is also a member of the Institute of Industrial and Systems Engineers (IISE) and the Institute for Operations Research and the Management Sciences (INFORMS).

Michael Biehler

Michael Biehler received his BS and MS in industrial and mechanical engineering from Karlsruhe Institute of Technology (KIT) in 2017 and 2020, respectively. Currently, he is a PhD candidate in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. His research rests at the interface between machine learning and cyber-physical (manufacturing) systems, where he aims to develop methods for cyber security and 3D machine learning, system monitoring, and control.

Xiaowei Yue

Dr. Xiaowei Yue got his PhD degree in industrial engineering, MS in statistics from Georgia Tech, MS in power engineering and thermophysics from Tsinghua University, BS in mechanical engineering from BIT. Currently, He is an assistant professor at the Grado Department of Industrial and Systems Engineering, Virginia Tech. His research interests focus on data analytics and quality control for advanced manufacturing. He is a recipient of Outstanding Young Manufacturing Engineer Award from SME. Dr. Yue serves as an associate editor for the IISE Transactions and the Journal of Intelligent Manufacturing. Dr. Yue is a senior member of IISE, ASQ and IEEE.

Jeffrey H. Hunt

Dr. Jeffrey H. Hunt received a BS degree in physics from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 1979, and an M.A. degree in physics and a Ph.D. degree in physics from the University of California at Berkeley, Berkeley, CA, USA, in 1981 and 1988, respectively. He is currently a Principal Scientist and a Senior Technical Fellow with Boeing Company, El Segundo, CA, USA. His career has included physics-based projects in condensed matter physics, quantum information sciences, surface science, and nonlinear optics and work on diverse applications, including both in defense sciences and commercial air and space technologies. He has published over 30 articles, three books, and two encyclopedia articles on condensed matter sciences. He holds 61 U.S. patents. His main research areas are wide scientific and technical challenges in aviation and aerospace industry, with particular applications in condensed matter sciences and nonlinear optics, composite aircraft assembly, and so on. Dr. Hunt is a fellow of the American Physical Society and the Optical Society of America.

Jianjun Shi

Dr. Jianjun Shi (https://sites.gatech.edu/jianjun-shi/) received BS and MS degrees in automation from the Beijing Institute of Technology in 1984 and 1987, respectively, and a PhD degree in mechanical engineering from the University of Michigan in 1992. Currently, Dr. Shi is the Carolyn J. Stewart Chair and Professor at the Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. His research interests include the fusion of advanced statistical and domain knowledge to develop methodologies for modeling, monitoring, diagnosis, and control for complex manufacturing systems. Dr. Shi is a Fellow of four professional societies, including ASME, IISE, INFORMS, and SME, an elected member of the International Statistics Institute (ISI), a life member of ASA, an Academician of the International Academy for Quality (IAQ), and a member of National Academy of Engineers (NAE).