On-machine measurement method and geometrical error analysis in a multi-step processing system of an ultra-precision complex spherical surface

Xing, Tianji; Zhao, Xuesen; Song, Luqi; Cui, Zhipeng; Zou, Xicong; Sun, Tao

doi:10.1016/j.jmapro.2022.05.057

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…Ultra-precision machining (UPM) is one of the most advanced fabrication methods to create a high-quality surface with a sub-micrometric form accuracy and nanometric surface roughness [1]. In UPM, there are inevitably various errors that affect the surface quality of the machined components, such as geometric errors [2,3], tool errors [4,5], thermal errors [6,7], dynamic errors [8,9], etc. The geometric errors, as a crucial contributor of up to 40-70%, majorly affect the form accuracy of a machined surface [10].…”

Section: Introductionmentioning

confidence: 99%

A Theoretical and Experimental Identification with Featured Structures for Crucial Position-Independent Geometric Errors in Ultra-Precision Machining

Zhang,

Zhang

2023

Machines

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In ultra-precision machining (UPM), position-independent geometric errors (PIGEs), i.e., squareness errors, have a crucial impact upon the form accuracy of a machined surface. Accordingly, more research work has been conducted in PIGE identification, to improve the form accuracy. However, the general identification methods were developed without consideration of the specific squareness errors for crucial PIGEs under the form errors of the machining process. Therefore, a new method with featured structures was proposed, to identify crucial PIGEs in UPM. Firstly, a volumetric error model was developed for PIGEs, to discuss the relationship between squareness errors and their resulting machining form errors. Secondly, following the developed model, some featured structures have been proposed with their machining form errors, to significantly indicate crucial PIGEs. Finally, a series of UPM and measuring experiments were conducted for the featured structures, and then their machining form errors were measured and extracted with specific squareness errors for the identification of crucial PIGEs. The theoretical and experimental results revealed that the proposed method is simple and efficient with the featured structures to accurately identify crucial PIGEs in UPM. Significantly, the study offers a deep insight into high-quality fabrication in UPM.

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