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


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.


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



Complexity penalty factor


Square error


Complexity penalty


Predicted squared error


Rake angle


Normal relief angle


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).


  1. 1.
    Rawangwong S, Chatthong J, Boonchouytan W, Burapa R (2014) Influence of cutting parameters in face milling semi-solid AA7075 using carbide tool affected the surface roughness and tool wear. Energy Procedia 56:448–457CrossRefGoogle Scholar
  2. 2.
    Ghani JA, Choudhury IA, Hassan HH (2004) Application of Taguchi method in the optimization of end milling parameters. J Mater Process Technol 145:84–92CrossRefGoogle Scholar
  3. 3.
    Shaji S, Radhakrishnan V (2003) Analysis of process parameters in surface grinding with graphite as lubricant based on the Taguchi method. J Mater Process Technol 141:51–59CrossRefGoogle Scholar
  4. 4.
    Lin SY, Lin JC, Lin CC, Jywe WY, Lin BJ (2006) Life prediction system using a tool’s geometric shape for high-speed milling. Int J Adv Manuf Technol 30:622–630CrossRefGoogle Scholar
  5. 5.
    Wang YC, Chen CH, Lee BY (2009) The predictive model of surface roughness and searching system in database for cutting tool grinding. Mater Sci Forum 626-627:11–16CrossRefGoogle Scholar
  6. 6.
    Yang TS (2008) The application of abductive networks and FEM to predict the limiting drawing ratio in sheet metal forming processes. Int J Adv Manuf Technol 37:58–69CrossRefGoogle Scholar
  7. 7.
    Sukumar MS, Venkata Ramaiah P, Nagarjuna A (2014) Optimization and prediction of parameters in face milling of Al-6061 using Taguchi and ANN approach. Procedia Eng 97:365–371CrossRefGoogle Scholar
  8. 8.
    Cui D, Zhang D, Wu B, Luo M (2017) An investigation of tool temperature in end milling considering the flank wear effect. Int J Mech Sci 131–132:613–624CrossRefGoogle Scholar
  9. 9.
    Zatarain M, Dombovari Z (2014) Stability analysis of milling with irregular pitch tools by the implicit subspace iteration method. Int J Dyn Control 2:26–34CrossRefGoogle Scholar
  10. 10.
    Lee BY, Tarng YS (2001) Surface roughness inspection by computer vision in turning operations. Int J Mach Tool Manu 41:1251–1263CrossRefGoogle Scholar
  11. 11.
    Lee BY, Juan H, Yu SF (2002) A study of computer vision for measuring surface roughness in turning process. Int J Adv Manuf Technol 19:295–301CrossRefGoogle Scholar
  12. 12.
    Yu SF, Lee BY, Lin WS (2001) Waveform monitoring of electric discharge machining by wavelet transform. Int J Adv Manuf Technol 17:339–343CrossRefGoogle Scholar
  13. 13.
    Karagiannis S, Stavropoulos P, Ziogas C, Kechagias J (2014) Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: An aluminium alloy 5083 case study. Proc Inst Mech Eng B J Manuf 228:233–244CrossRefGoogle Scholar
  14. 14.
    Juan H, Yu SF, Lee BY (2003) The optimal cutting-parameter selection of production cost in HSM for SKD61 tool steels. Int J Mach Tool Manu 43:679–686CrossRefGoogle Scholar
  15. 15.
    Philip D (1982) Harvey, engineering properties of steels. American Society for Metals, Metals Park, OHIO, pp 293–422Google Scholar
  16. 16.
    Juneja BL (2003) Fundamentals of metal cutting and machine tools. New Age Int 2003:134–137Google Scholar

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

Personalised recommendations