Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum.

Zhichao Sheng, Jian Xiong, Zhijing Zhang, Taiyu Su, Min Zhang, Qimuge Saren, Xiao Chen

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Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Zhichao ShengSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.ORCID 0009-0002-9195-0097

Jian XiongSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0003-3822-9997

Zhijing ZhangSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0002-4305-9761

Taiyu SuSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Min ZhangGenertec Machine Tool Engineering Research Institute Co., Ltd., Beijing 101300, China.

Qimuge SarenSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Xiao ChenSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Funding

National Natural Science Foundation of China 52205512National Natural Science Foundation of China U22B2088

6 · The paper itself

Abstract

The geometric error distributed on components' contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution error modeling for precision machining surfaces based on the K-space spectrum is proposed. To determine the boundary of systematical error and random error, we used a cruciform boundary line method based on the K-space spectrum, achieving the optimal separation of the two with frequency difference. The effectiveness of the method was experimentally verified using two sets of machined surfaces. By comparing with current common random error filtering methods, the outstanding role of the proposed error separation method in separating random error and preserving processing features has been verified.

Indexed as

error separationgeometric distribution errorK-space spectrumrandom error filteringskin model

Identifiers

PMID39771802

DOI10.3390/s24248067

PMCPMC11679476

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