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Micro milling of titanium alloy Ti-6Al-4V: 3-D finite element modeling for prediction of chip flow and burr formation

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Abstract

Cutting process of titanium alloy Ti-6Al-4V is considered difficult due to chemical affinity between tool and work material, adhesion, built-up edge and burr formation, and tool wear resulting in loss of productivity. Three dimensional (3-D) chip flow together with local field variables such as temperature, elastic/plastic strain, strain-rate and velocity in the shear zones during micro milling process can be predicted using continuum-mechanics based 3-D Finite Element (FE) modelling and simulation of elastic/viscoplastic work material deformations. This paper provides much needed process insight for chip flow, built-up edge and burr formation by using modeling work with experimental validation. Scanning electron microscopic (SEM) observation of the 3-D chip morphology and burrs demonstrate ductile fractured surfaces together with localized instability and failure behaviors. FE simulations are utilized to investigate the effects of micro milling operation i.e. up and down milling and tool edge radius on 3-D chip flow, built-up edge, and 3-D burr formation. Simulated results are compared with measurements of chip morphology, shape, and dimensions together with tool edge condition of built-up edge and chip adhesion yielding to good agreements.

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Acknowledgements

The support provided by DEFORM software by SFTC, Ohio, USA is gratefully acknowledged.

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Authors and Affiliations

  1. Manufacturing and Automation Research Laboratory, Rutgers, The State University of New Jersey Industrial and Systems Engineering, 96 Frelinghuysen Road, Piscataway, NJ, 08854, USA

    Tuğrul Özel & Alaa Olleak

  2. Department of Industrial Engineering, Burapha University, Chonburi, Thailand

    Thanongsak Thepsonthi

Authors
  1. Tuğrul Özel
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  2. Alaa Olleak
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  3. Thanongsak Thepsonthi
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Correspondence to Tuğrul Özel.

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Özel, T., Olleak, A. & Thepsonthi, T. Micro milling of titanium alloy Ti-6Al-4V: 3-D finite element modeling for prediction of chip flow and burr formation. Prod. Eng. Res. Devel. 11, 435–444 (2017). https://doi.org/10.1007/s11740-017-0761-4

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  • DOI: https://doi.org/10.1007/s11740-017-0761-4

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