Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook
Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook
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DOI：
10.3390/machines9010013
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发表时间：
2021-01
期刊：
影响因子：
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通讯作者：
Yuhang Yang;Zhiqiao Dong;Yuquan Meng;Chenhui Shao
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文献类型：
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作者：
Yuhang Yang;Zhiqiao Dong;Yuquan Meng;Chenhui Shao
在智能制造时代，对空间和时空过程的高保真表征和有效监控对于许多制造过程和系统的高性能质量控制至关重要。虽然测量技术的最新发展已经使得获取高分辨率三维（3D）表面测量数据成为可能，但是在现实世界的生产环境中使用这种技术通常是昂贵且耗时的。源于统计和机器学习的数据驱动方法可以实现智能、经济高效的表面测量，从而使制造商能够使用高分辨率表面数据进行更好的决策，而不会引入由数据采集引起的大量生产成本。在这些方法中，空间和时空内插技术可以使用其他位置的测量来推断表面上未测量的位置，从而降低测量成本和时间。然而，插值方法对测量数据的可用性非常敏感，并且它们的性能在很大程度上取决于测量方案或采样设计，即，如何分配测量工作。因此，采样设计被认为是实现智能表面测量的另一个重要领域。本文回顾和总结了各种制造应用中曲面测量插值和采样设计的最新研究成果。研究差距和未来的研究方向也被确定，并可以作为一个基本的指导方针，工业从业人员和研究人员在这些领域的未来研究。
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.