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Development of a generalized cutting force prediction model for carbon fiber reinforced polymers based on rotary ultrasonic face milling

  • Muhammad Amin
  • Songmei Yuan
  • Muhammad Zubair Khan
  • Qi Wu
  • Guangyuan Zhu
ORIGINAL ARTICLE

Abstract

Carbon fiber reinforced polymers (CFRP) have got paramount importance in aircraft, aerospace, and other fields due to their attractive properties of high specific strength/stiffness, high corrosion resistance, and low thermal expansion. These materials have also the properties of inhomogeneity, heterogeneity, anisotropy, and low heat dissipation which generate the issues of excessive cutting forces and machining damages (delamination, fiber pull out, matrix burning, etc.). The cutting forces are required to be modeled for their control/ minimization. In this research, a generalized cutting force model has been developed for rotary ultrasonic face milling of CFRP composites. The experimental machining was carried out on CFRP-T700 material. The cutting forces found decreased significantly with the increase of spindle speed while the same found increased with the increase of feed rate and cutting depth. The variation less than 10% has been found between experimental and simulated values (from the model) of cutting forces. However, the higher variation has also observed in the few groups of experiments due to the properties of inhomogeneity, heterogeneity, anisotropy, and low heat dissipation of such materials. The expression for the contact area of the abrasive core tools has been improved and an overlapping cutting allowance has been incorporated the first time. The developed cutting force model has been validated and found robust. So, the generalized cutting force model developed in this paper can be applied to control/minimize the cutting forces for rotary ultrasonic face milling of CFRP composite materials and optimization of the process.

Keywords

Rotary ultrasonic face milling Carbon fiber reinforced polymers CFRP-T700 Cutting force Brittle fracture Machining parameters 

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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • Muhammad Amin
    • 1
    • 2
  • Songmei Yuan
    • 1
  • Muhammad Zubair Khan
    • 2
  • Qi Wu
    • 1
  • Guangyuan Zhu
    • 1
  1. 1.School of Mechanical Engineering and AutomationBeihang University, Beijing Engineering Technological Research Center of High-efficient and Green CNC Machining Process and EquipmentBeijingChina
  2. 2.Department of Mechanical EngineeringInstitute of Space TechnologyIslamabadPakistan

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