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Review of self-referenced measurement algorithms: Bridging lateral shearing interferometry and multi-probe error separation |
Dede ZHAI, Shanyong CHEN, Ziqiang YIN, Shengyi LI() |
College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, China; Hu’nan Key Laboratory of Ultra-precision Machining Technology, Changsha 410073, China |
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Abstract With the development of new materials and ultra-precision processing technology, the sizes of measured objects increase, and the requirements for machining accuracy and surface quality become more exacting. The traditional measurement method based on reference datum is inadequate for measuring a high-precision object when the quality of the reference datum is approximately within the same order as that of the object. Self-referenced measurement techniques provide an effective means when the direct reference-based method cannot satisfy the required measurement or calibration accuracy. This paper discusses the reconstruction algorithms for self-referenced measurement and connects lateral shearing interferometry and multi-probe error separation. In lateral shearing interferometry, the reconstruction algorithms are generally categorized into modal or zonal methods. The multi-probe error separation techniques for straightness measurement are broadly divided into two-point and three-point methods. The common features of the lateral shearing interferometry method and the multi-probe error separation method are identified. We conclude that the reconstruction principle in lateral shearing interferometry is similar to the two-point method in error separation on the condition that no yaw error exists. This similarity may provide a basis or inspiration for the development of both classes of methods.
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Keywords
self-referenced measurement
lateral shearing interferometry
multi-probe error separation
surface metrology
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Corresponding Author(s):
Shengyi LI
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Just Accepted Date: 15 February 2017
Online First Date: 22 March 2017
Issue Date: 19 June 2017
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