A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

Chunlei He; Jiwang Yan; Shuqi Wang; Shuo Zhang; Guang Chen; Chengzu Ren

doi:10.1088/2631-7990/acdb0a

A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

Chunlei He
, Jiwang Yan
, Shuqi Wang
, Shuo Zhang
, Guang Chen
, Chengzu Ren

機械工学科

研究成果: Article › 査読

55 被引用数 (Scopus)

抄録

Highlight A hybrid surface roughness model combining the theoretical model and deep learning method are suggested. SSGB and GB roughness component reduce due to a decrease of misorientation angle. Transcrystalline/intercrystalline fracture mode of work material is analyzed at different tool sharpness. A flat copper surface finish of Sa 1.314 nm without SSGB is attained.

本文言語	English
論文番号	035102
ジャーナル	International Journal of Extreme Manufacturing
巻	5
号	3
DOI	https://doi.org/10.1088/2631-7990/acdb0a
出版ステータス	Published - 2023 9月

ASJC Scopus subject areas

産業および生産工学

文献へのアクセス

10.1088/2631-7990/acdb0a

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引用スタイル

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title = "A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials",

abstract = "Highlight A hybrid surface roughness model combining the theoretical model and deep learning method are suggested. SSGB and GB roughness component reduce due to a decrease of misorientation angle. Transcrystalline/intercrystalline fracture mode of work material is analyzed at different tool sharpness. A flat copper surface finish of Sa 1.314 nm without SSGB is attained.",

keywords = "diamond turning, material-defect roughness component, neural network, polycrystalline copper, simulated annealing algorithm",

author = "Chunlei He and Jiwang Yan and Shuqi Wang and Shuo Zhang and Guang Chen and Chengzu Ren",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by IOP Publishing Ltd on behalf of the IMMT.",

year = "2023",

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doi = "10.1088/2631-7990/acdb0a",

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T1 - A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

AU - He, Chunlei

AU - Yan, Jiwang

AU - Wang, Shuqi

AU - Zhang, Shuo

AU - Chen, Guang

AU - Ren, Chengzu

PY - 2023/9

Y1 - 2023/9

N2 - Highlight A hybrid surface roughness model combining the theoretical model and deep learning method are suggested. SSGB and GB roughness component reduce due to a decrease of misorientation angle. Transcrystalline/intercrystalline fracture mode of work material is analyzed at different tool sharpness. A flat copper surface finish of Sa 1.314 nm without SSGB is attained.

AB - Highlight A hybrid surface roughness model combining the theoretical model and deep learning method are suggested. SSGB and GB roughness component reduce due to a decrease of misorientation angle. Transcrystalline/intercrystalline fracture mode of work material is analyzed at different tool sharpness. A flat copper surface finish of Sa 1.314 nm without SSGB is attained.

KW - diamond turning

KW - material-defect roughness component

KW - neural network

KW - polycrystalline copper

KW - simulated annealing algorithm

UR - https://www.scopus.com/pages/publications/85163437848

UR - https://www.scopus.com/pages/publications/85163437848#tab=citedBy

U2 - 10.1088/2631-7990/acdb0a

DO - 10.1088/2631-7990/acdb0a

M3 - Article

AN - SCOPUS:85163437848

SN - 2631-8644

VL - 5

JO - International Journal of Extreme Manufacturing

JF - International Journal of Extreme Manufacturing

IS - 3

M1 - 035102

ER -

A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

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