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.
Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing
Manuscript received December 27, 2016; final manuscript received March 16, 2017; published online August 24, 2017. Assoc. Editor: Robert Gao.
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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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