Development of cutting force prediction model for vibration-assisted slot milling of carbon fiber reinforced polymers

  • Muhammad Amin
  • Songmei YuanEmail author
  • Asif Israr
  • Li Zhen
  • Wu Qi


Carbon fiber reinforced polymers (CFRP) have got rapidly increased applications in aerospace/aircraft and other fields due to their attractive properties of high specific strength/stiffness, high corrosion resistance, and low thermal expansion. These materials have also some challenging properties like heterogeneity, anisotropy, and low heat dissipation. Due to these properties, the issues of excessive cutting forces and machining damages (delamination, fiber pull-out, surface/subsurface defects, etc.) are encountered in machining. The cutting forces are required to be minimized for qualified machining with reduced damages. In this research, a novel cutting force prediction model has been developed for vibration-assisted slot milling. The experimental machining has been carried out on CFRP-T700 composite material. The effective cutting time per vibration cycle and the force of friction have been expressed/calculated. The feasibility of vibration-assisted machining for CFRP composites has also been evaluated. The relationships of the axial and feed cutting forces with machining parameters were investigated. The results have shown the variations below 10% among experimental and corresponding simulation values (from the model) of cutting forces. However, the higher variations have been found in some experiments which are mainly due to heterogeneity, anisotropy, and some other properties of such materials. The developed cutting force model then validated through pilot experiments and found the same results. So, the developed cutting force model is robust and can be applied to predict cutting forces and optimization for vibration-assisted slot milling of CFRP composite materials at the industry level.


Vibration-assisted slot milling Cutting force Prediction model Carbon fiber reinforced polymers CFRP-T700 Machining parameters 


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This research is financially supported by National High Technology Research and Development Program of China under program no. 863 with grant no.2013AA040105. The authors are indebted to this financial support to accomplish this research work.

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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • Muhammad Amin
    • 1
    • 2
  • Songmei Yuan
    • 1
    Email author
  • Asif Israr
    • 2
  • Li Zhen
    • 1
  • Wu Qi
    • 1
  1. 1.School of Mechanical Engineering & Automation, Beijing Engineering Technological Research Center of High-efficient & Green CNC Machining Process and EquipmentBeihang UniversityBeijingChina
  2. 2.Department of Mechanical EngineeringInstitute of Space TechnologyIslamabadPakistan

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