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Trochoidal milling: investigation of dynamic stability and time domain simulation in an alternative path planning strategy

  • Farbod Akhavan Niaki
  • Abram Pleta
  • Laine Mears
  • Nils Potthoff
  • Jim A. Bergmann
  • Petra Wiederkehr
ORIGINAL ARTICLE
  • 18 Downloads

Abstract

Trochoidal milling is an alternative path planning strategy with the potential of increasing material removal rate per unit of tool wear and therefore productivity cost while reducing cutting energy and improving tool performance. These characteristics in addition to low radial immersion of the tool make trochoidal milling a desirable tool path in machining difficult-to-cut alloys such as nickel-based superalloys. The objective of this work is to study the dynamic stability of trochoidal milling and investigate the interaction of tool path parameters with stability behavior when machining IN718 superalloy. While there exist a few published works on dynamics of circular milling (an approximated tool path for trochoidal milling), this work addresses the dynamics of the actual trochoidal tool path. First, the chip geometry quantification strategy is explained, then the chatter characteristic equation in trochoidal milling is formulated, and chatter stability lobes are generated. It is shown that unlike a conventional end-milling operation where the geometry of chips remains constant during the cut (resulting in a single chatter diagram representing the stability region), trochoidal milling chatter diagrams evolve in time with the change in geometry (plus cutter entering and exiting angles) of each chip. The limit of the critical depth of cut is compared with conventional end milling and shown that the depth of cut can be increased up to ten times while preserving stability. Finally, the displacement response of the cutting tool is simulated in the time domain for stable and unstable cutting regions; numerical simulation and theoretical results are compared.

Keywords

Trochoidal milling Dynamic stability Superalloys IN718 

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Notes

Funding information

The authors would like to thank the National Science Foundation for supporting this work under Grant No. 1760809.

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Disclaimer

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Farbod Akhavan Niaki
    • 1
  • Abram Pleta
    • 1
  • Laine Mears
    • 1
  • Nils Potthoff
    • 2
  • Jim A. Bergmann
    • 2
  • Petra Wiederkehr
    • 2
  1. 1.International Center for Automotive ResearchClemson UniversityGreenvilleUSA
  2. 2.LS14, Virtual MachiningTU Dortmund UniversityDortmundGermany

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