Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

Chenhui Shao, Jionghua Judy Jin, S. Jack Hu

Research output: Contribution to journalArticlepeer-review


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 threedimensional (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.

Original languageEnglish (US)
Article number101002
JournalJournal of Manufacturing Science and Engineering, Transactions of the ASME
Issue number10
StatePublished - Oct 1 2017


  • dynamic sampling design
  • intelligent sensing
  • manufacturing
  • spatiotemporal processes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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