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Research Papers

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

[+] Author and Article Information
Chenhui Shao

Department of Mechanical Science
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

S. Jack Hu

Department of Mechanical Engineering,
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 139(10), 101002 (Aug 24, 2017) (11 pages) Paper No: MANU-16-1678; doi: 10.1115/1.4036347 History: Received December 27, 2016; Revised March 16, 2017

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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References

Figures

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Fig. 1

Tool surface evolution in ultrasonic metal welding [3]

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Fig. 2

Illustration of grid segmentation for level 2 measurement

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Fig. 3

Flowchart for the estimation and monitoring of ϕt

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Fig. 4

Heat map of the weight matrix in the simulation study

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Fig. 5

Performance comparison of candidate sampling methods in the simulation study: (a) measurement cost, (b) prediction precision, (c) loss, and (d) prediction RMSE

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Fig. 6

Heat map of level 2 measurement distribution in the simulation study: (a) method 1, (b) method 2, (c) method 3, and (d) method 4

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Fig. 7

Heat map of the average prediction variance in the simulation study: (a) method 1, (b) method 2, (c) method 3, and (d) method 4

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Fig. 8

Spatiotemporal progression of an anvil surface in ultrasonic metal welding

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Fig. 9

Anvil surface: (a) contour plot and (b) weight heat map

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Fig. 10

Precision comparison of dynamic and random sampling approaches for ultrasonic metal welding

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Fig. 11

Heat map of level 2 measurement distribution for ultrasonic metal welding: (a) random sampling and (b) dynamic sampling

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Fig. 12

Heat map of the average prediction variance for ultrasonic metal welding: (a) random sampling and (b) dynamic sampling

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