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 three-dimensional (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 (GA) 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.
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October 2017
Research-Article
Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing
Chenhui Shao,
Chenhui Shao
Department of Mechanical Science
and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: chshao@illinois.edu
and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: chshao@illinois.edu
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Jionghua (Judy) Jin,
Jionghua (Judy) Jin
Department of Industrial and
Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jhjin@umich.edu
Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jhjin@umich.edu
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S. Jack Hu
S. Jack Hu
Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jackhu@umich.edu
University of Michigan,
Ann Arbor, MI 48109
e-mail: jackhu@umich.edu
Search for other works by this author on:
Chenhui Shao
Department of Mechanical Science
and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: chshao@illinois.edu
and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: chshao@illinois.edu
Jionghua (Judy) Jin
Department of Industrial and
Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jhjin@umich.edu
Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jhjin@umich.edu
S. Jack Hu
Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jackhu@umich.edu
University of Michigan,
Ann Arbor, MI 48109
e-mail: jackhu@umich.edu
1Corresponding author.
Manuscript received December 27, 2016; final manuscript received March 16, 2017; published online August 24, 2017. Assoc. Editor: Robert Gao.
J. Manuf. Sci. Eng. Oct 2017, 139(10): 101002 (11 pages)
Published Online: August 24, 2017
Article history
Received:
December 27, 2016
Revised:
March 16, 2017
Citation
Shao, C., Jin, J. (., and Jack Hu, S. (August 24, 2017). "Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing." ASME. J. Manuf. Sci. Eng. October 2017; 139(10): 101002. https://doi.org/10.1115/1.4036347
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