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Prediction model of cutting edge for end mills based on mechanical material properties

  • Jenn-Yih Chen
  • Tzu-Chi ChanEmail author
  • Bean-Yin Lee
  • Chiao-Yun Liang
ORIGINAL ARTICLE
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Abstract

In this study, we use an abductory induction mechanism (AIM) polynomial network, with material properties provided as input, to generate a model that predicts tool geometry. The resulting model allows for the rapid and accurate design of suitable tool geometries while using different materials. Here, we report the results for the tool geometry predicted by our model obtained when different materials are used; the materials examined are NAK80, quenched SKD61, annealed SKD61, quenched S45C, annealed S45C, SUS316L, SUS304, and Ti-6Al-4 V. After cutting, the flatness of the material is measured using a tool microscope to determine its flank. Whether the abnormal cutting phenomenon is caused in the form of a springback or chipping caused by material rebound is also investigated. The results confirm that the tool belongs to a normal cutting phenomenon. The fitting state of the model is adjusted to ensure an appropriate fit. We also verified the prediction model for two untested materials: SCM440 and SUS420. The polynomial network model predicts that SCM440 has a normal relief angle of 6.25°, a tool wedge angle of 76.47°, a predicted normal ISS420 with a normal relief angle of 3.46°, and a tool wedge angle of 68.72°. The value of the flatness of the cutting edge is determined to be within 5 μm, indicating that the flank surface has normal friction phenomenon.

Keywords

Mechanical properties Tool geometry Abductory induction mechanism (AIM) polynomial network Tool wedge angle Normal relief angle 

Nomenclature

CPM

Complexity penalty factor

FSE

Square error

KP

Complexity penalty

PSE

Predicted squared error

αn

Rake angle

γn

Normal relief angle

Notes

Funding information

The authors are grateful to the Ministry of Science and Technology of the R.O.C. for supporting this research (Grant Number MOST 107-2218-E-150-005-MY3).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  • Jenn-Yih Chen
    • 1
  • Tzu-Chi Chan
    • 1
    Email author
  • Bean-Yin Lee
    • 1
  • Chiao-Yun Liang
    • 1
  1. 1.Department of Mechanical and Computer-Aided EngineeringNational Formosa UniversityYunlin CountyRepublic of China

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