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Journal of Mechanical Science and Technology

, Volume 33, Issue 11, pp 5369–5374 | Cite as

Prediction model of the surface roughness of micro-milling single crystal copper

  • Xiaohong LuEmail author
  • Liang Xue
  • Feixiang Ruan
  • Kun Yang
  • Steven Y. Liang
Article
  • 4 Downloads

Abstract

Presently, the demand for single crystal copper micro-components is increasing in various fields because single crystal copper has good electrical conductivity. Micro-milling technology is an effective processing technology for small single crystal copper parts. Surface roughness is a key performance indicator for micro-milling single crystal copper. Establishing a surface roughness prediction model with high precision is useful to guarantee the processing quality by selecting the cutting parameters for micro-milling. Few studies have currently focused on micro-milling single crystal copper. In this study, the orthogonal experiments of micro-milling single crystal copper were conducted, and the influences of the spindle and feed speeds and axial depth of cut on the surface roughness of micro-milled single crystal copper with different orientations were analyzed by range analyses. The spindle rotation speed was the major affecting factor. The surface roughness of single crystal copper in different crystal orientations was predicted by using the SVM method. Experimental results showed that the average relative error of the surface roughness of <100>, <110>, and <111> crystal orientation single crystal copper was 2.7 %, 3.3 %, and 2.2 %, respectively, and that the maximum relative errors were 7.0 %. 10.1 %, and 3.1 %, respectively. The uncertainty analysis was conducted by using the Monte Carlo method to verify the reliability of the built surface roughness model.

Keywords

Micro-milling Prediction model Single crystal copper Surface roughness 

Nomenclature

ap

Depth of cut

C

Penalty parameter

fz

Feed per tooth

g

Gamma, a parameter in RBF

n

Entropy

Ra

Surface roughness

RBF

Radial basis kernel function

SVM

Support vector machine

SVMR

Support vector machine regression

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Notes

Acknowledgments

The study is supported by the National Natural Science Foundation of China under Grant No. 51875080 and Liaoning Natural Science Foundation Project under Grant No. 2019- MS-038. The financial contributions are gratefully acknowledged.

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Copyright information

© KSME & Springer 2019

Authors and Affiliations

  • Xiaohong Lu
    • 1
    Email author
  • Liang Xue
    • 1
  • Feixiang Ruan
    • 1
  • Kun Yang
    • 1
  • Steven Y. Liang
    • 2
  1. 1.Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of EducationDalian University of TechnologyDalianChina
  2. 2.The George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA

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