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  • 刘壮 ( 副教授 )

    的个人主页 http://faculty.nuaa.edu.cn/lz5/zh_CN/index.htm

  •   副教授   硕士生导师
  • 招生学科专业:
    机械工程 -- 【招收硕士研究生】 -- 机电学院
    航空宇航科学与技术 -- 【招收硕士研究生】 -- 机电学院
    机械 -- 【招收硕士研究生】 -- 机电学院
论文成果 当前位置: 中文主页 >> 科学研究 >> 论文成果
Prediction of Surface Roughness in Abrasive Assisted Electrochemical Jet Machining of Micro-Channels

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所属单位:机电学院
发表刊物:INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE
关键字:abrasive assisted electrochemical jet machining electrochemical jet machining micro-channel surface roughness
摘要:Abrasive assisted electrochemical jet machining (AECJM) is capable of fabricating micro-channels effectively at metal surface through erosion, corrosion and synergistic effect simultaneously. The complex mechanism of material removal results in significant difficulties to describe surface topography and predict surface roughness of a micro-channel produced by AECJM. This paper established a mathematical model for accurately predicting areal roughness (S-a) of micro-channels due to AECJM of SS304. Six main factors, i.e. working voltage (U), concentration of abrasive (C-a), electrolyte concentration (C-s), jet pressure (P), jet scan speed (V) and passes (N) have been considered in establishing the model. An orthogonal experiment was conducted to investigate the effects of the six main factors and two-factor interactions on 5', through regression analysis. The results reveal that the main factors influence the mean 5', with an importance of order as V-N-C-a-C-s-P-U. Working voltage, jet pressure and six two-factor interactions exhibit unmarked influences on the S-a, and have been eliminated from the model for purpose of simplification. The validation showed that the prediction agrees with experimental data with a maximum error of 12.2% and an average error of 3.5%.
ISSN号:1452-3981
是否译文:否
发表时间:2018-06-01
合写作者:Zhao, Kai,Gao, Changshui,Guo, Chao
通讯作者:刘壮

 

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