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Analytical and Numerical Predictions of Machining-Induced Residual Stress in Milling of Inconel 718 Considering Dynamic Recrystallization

[+] Author Affiliations
Yixuan Feng, Zhipeng Pan, Steven Y. Liang

Georgia Institute of Technology, Atlanta, GA

Xiaohong Lu

Dalian University of Technology, Dalian, China

Paper No. MSEC2018-6386, pp. V004T03A023; 11 pages
doi:10.1115/MSEC2018-6386
From:
  • ASME 2018 13th International Manufacturing Science and Engineering Conference
  • Volume 4: Processes
  • College Station, Texas, USA, June 18–22, 2018
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5138-8
  • Copyright © 2018 by ASME

abstract

A new analytical model is proposed to predict the residual stress in the milling process of Inconel 718 based upon the mechanics analysis of microstructural evolutions. The model proposes to quantify the effects of dynamic recrystallization process on the material flow stress under combined thermal-mechanical loadings in machining. Physics-based mechanistic model is applied to predict the percentage of dynamic recrystallization and the grain size as functions of the milling process parameters and materials constative attributes. The variation of grain size is expected to alter the yield stress, and such dependency relationship is applied to predict the flow stress, which is also dependent on strain, strain rate, and temperature. The time-varying trajectory of residual stress is then predicted at each milling rotation angle through the transformation from milling to equivalent orthogonal cutting, the calculation of stress distribution in loading process, and the stress change during relaxation. The results of analytical model are validated through numerical prediction. The residual stress profile predicted by proposed analytical model matches better with results from numerical model comparing with model without consideration of dynamic recrystallization, especially within subsurface area, with improved accuracy of peak compressive residual stress prediction.

Copyright © 2018 by ASME

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