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This paper reviews knife-edge interferometry (KEI) capable of inspection and metrology for various engineering applications, including displacement measuring sensors for dynamic system controls and edge quality of the parts, such as cutting tools, corrosive blades, and photomask patterns. This paper includes the modeling, design, and data analysis of KEI. With the expanding market of manufacturing industries, edge topography and instrumentation technology become more and more vital to industrial manufacturing-related applications such as cutting tool wear inspection, photomask edge roughness determination, and edge corrosion propagation monitoring. Due to the limitation of measurement requirements like non-contact (photomask inspection), in-situ (cutting tool inspection), and real-time (corrosion propagation monitoring), there are only a few methods available in the market above, and those methods are based on post-processing. The KEI is capable of on-machine measurements, especially for the nanopositioning systems, providing a large working range and positioning accuracy compared with the conventional displacement sensor. This review addresses the current and future KEI technology. Here, including the theoretical approaches to KEI, this review details the data analy 

This paper presents a novel noncontact measurement and inspection method based on knife-edge diffraction theory for corrosive wear propagation monitoring at a sharp edge. The degree of corrosion on the sharp edge was quantitatively traced in process by knife-edge interferometry (KEI). The measurement system consists of a laser diode, an avalanche photodiode, and a linear stage for scanning. KEI utilizes the interferometric fringes projected on the measurement plane when the light is incident on a sharp edge. The corrosion propagation on sharp edges was characterized by analyzing the difference in the two interferometric fringes obtained from the control and measurement groups. By using the cross-correlation algorithm, the corrosion conditions on sharp edges were quantitatively quantified into two factors: lag and similarity for edge loss and edge roughness, respectively. The KEI sensor noise level was estimated at 0.03% in full scale. The computational approach to knife-edge diffraction was validated by experimental validation, and the computational error was evaluated at less than 1%. Two sets of razor blades for measurement and control groups were used. As a result, the lag will be increased at an edge loss ratio of 1.007/µm due to the corrosive wear, while the similarity will be decreased at a ratio of$5.4\times <\#comment/>{10}^{-<\#comment/>4}/\text{µ<\#comment/>}\mathrm{m}$with respect to edge roughness change. Experimental results showed a good agreement with computational results.