Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, it is generally expensive to acquire high-resolution spatiotemporal data in manufacturing due to the high cost of the 3D measurement system or the time-consuming measurement process. In this paper, we develop a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to predictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the surface progression rate. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.
Skip Nav Destination
ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
June 4–8, 2017
Los Angeles, California, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-5074-9
PROCEEDINGS PAPER
Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing Available to Purchase
Chenhui Shao,
Chenhui Shao
University of Illinois at Urbana-Champaign, Urbana, IL
Search for other works by this author on:
Jionghua (Judy) Jin,
Jionghua (Judy) Jin
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
S. Jack Hu
S. Jack Hu
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
Chenhui Shao
University of Illinois at Urbana-Champaign, Urbana, IL
Jionghua (Judy) Jin
University of Michigan, Ann Arbor, MI
S. Jack Hu
University of Michigan, Ann Arbor, MI
Paper No:
MSEC2017-2695, V003T04A004; 11 pages
Published Online:
July 24, 2017
Citation
Shao, C, Jin, J(, & Hu, SJ. "Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing." Proceedings of the ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. Volume 3: Manufacturing Equipment and Systems. Los Angeles, California, USA. June 4–8, 2017. V003T04A004. ASME. https://doi.org/10.1115/MSEC2017-2695
Download citation file:
21
Views
Related Proceedings Papers
Related Articles
Kalman Smoother Based Force Localization and Mapping Using Intravital Video Microscopy
J. Dyn. Sys., Meas., Control (November,2010)
Three-Dimensional Optical Measurements of Porous Foams
J. Manuf. Sci. Eng (November,2006)
Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing
J. Manuf. Sci. Eng (October,2017)
Related Chapters
Motion Analysis for Multilayer Sheets
Ultrasonic Welding of Lithium-Ion Batteries
Manufacturing and Remanufacturing Problem Based on Lot-Sizing Plannin
International Conference on Advanced Computer Theory and Engineering, 4th (ICACTE 2011)
Defining Joint Quality Using Weld Attributes
Ultrasonic Welding of Lithium-Ion Batteries