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An analytical prediction model of surface topography generated in 4-axis milling process

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Abstract

The machined surface topography of workpiece plays an important role in the performance and service life of workpiece. At present, the study of milling surface topography is mainly on 3-axis milling with ball end mill. Also, in 4/5-axis milling, surface topography analysis is mainly on the experimental data. In order to solve this problem, an analytical prediction model of milling surface topography is proposed, which can obtain the machined workpiece surface topography directly from cutting parameters, cutter location file, and workpiece surface geometry. The effects of cutting parameters on surface roughness are discussed, such as cutting velocity, feed speed, and lead angle. Different 4-axis milling experiment conditions are set up to validate the proposed model. The results show that the prediction results agree with experiment results. Also, this method can be used to predict the surface topography in five-axis milling and optimize the cutting parameters in the further.

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All data generated or analyzed during this study are included in this published article.

Funding

This study is supported by the Hubei Superior and Distinctive Discipline Group of “Mechatronics and Automobiles” (XKQ2021037).

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Authors and Affiliations

  1. Xiangyang Key Laboratory of Intelligent Manufacturing and Machine Vision, School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China

    Ruihu Zhou

  2. Shenzhen Sophway Technology Co., Ltd., Shenzhen, 518108, China

    Qinlin Chen

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  1. Ruihu Zhou
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  2. Qinlin Chen
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Contributions

Authorship specific contributions: Ruihu Zhou: methodology, writing—original draft preparation, Qilin Chen: experimental work. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Ruihu Zhou.

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Zhou, R., Chen, Q. An analytical prediction model of surface topography generated in 4-axis milling process. Int J Adv Manuf Technol 115, 3289–3299 (2021). https://doi.org/10.1007/s00170-021-07410-x

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  • DOI: https://doi.org/10.1007/s00170-021-07410-x

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