Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook: Review and outlook

Yuhang Yang, Zhiqiao Dong, Yuquan Meng, Chenhui Shao

Research output: Contribution to journalReview articlepeer-review


High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

Original languageEnglish (US)
Article number13
Pages (from-to)1-33
Number of pages33
Issue number1
StatePublished - Jan 2021


  • 3D surface measurement
  • Big data analytics
  • Data fusion
  • Data-driven methods
  • Intelligent metrology
  • Interpolation
  • Machine learning
  • Measurement strategy
  • Quality control
  • Sampling design
  • Smart manufacturing
  • Spatial process
  • Spatiotemporal process

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Mechanical Engineering
  • Control and Optimization
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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