Abstract
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.
Highlights
Spatial and spatiotemporal processes are ubiquitous across all scales in manufacturing.They can manifest themselves in critical product quality characteristics or degradation of consumable tools
The automotive industry had been using coordinate measuring machines (CMMs) for quality inspection of engine machining, but CMMs cannot adequately capture some small-scale variation patterns, e.g., the local distortions around the cylinder bores, which can be well characterized by the high-resolution surface measurement data acquired by laser holographic interferometer (LHI)
It is common to adopt function spaces and relevant criteria which are different from those of previous sampling and reproducing kernel Hilbert space (RKHS)-related methods. This leads to various techniques for curve fitting, which are common in the statistical learning literature, and widely used for to process various data
Summary
Spatial and spatiotemporal processes are ubiquitous across all scales in manufacturing.They can manifest themselves in critical product quality characteristics (e.g., surface quality in machining [1,2,3,4], geometric compliance [5,6] and surface finish/texture [7] in additive manufacturing) or degradation of consumable tools (e.g., cutting and lapping tools in machining [8], horn and anvil in ultrasonic welding [9,10,11,12]). Spatial and spatiotemporal processes are ubiquitous across all scales in manufacturing. The automotive industry had been using coordinate measuring machines (CMMs) for quality inspection of engine machining, but CMMs cannot adequately capture some small-scale variation patterns, e.g., the local distortions around the cylinder bores, which can be well characterized by the high-resolution surface measurement data acquired by LHI. The availability of such data has helped reveal new insights into the cutting dynamics and develop effective variation control methods, e.g., [1,2]
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