CoChat
Sign Up
Cited by 36Advanced machining processes and optimizationAdvanced Machining and Optimization TechniquesEngineering Technology and Methodologies

The DOI matches its Crossref record.

Experimental Parametric Relationships for Chip Geometry in Dry Machining of the Ti6Al4V Alloy

Open access
Sánchez Hernández, Yezika, Trujillo Vilches, Francisco Javier, Bermudo Gamboa, Carolina, Sevilla Hurtado, Lorenzo·2018·Materials
DOI 10.3390/ma11071260PMC PMC6073982
Open this paper in CoChat — free accountOpen PDF

Abstract

The Ti6Al4V alloy is included in the group of difficult-to-cut materials. Segmented chips are generated for a wide range of cutting parameters. This kind of chip geometry leads to the periodic variation of machining forces, tool vibrations, and work part-tolerance inaccuracies. Therefore, the analysis of chip morphology and geometry becomes a fundamental machinability criterion. However, few studies propose experimental parametric relationships that allow predicting chip-geometry evolution as a function of cutting parameters. In this work, an experimental analysis of the influence of cutting speed and feed rate on various chip-geometric parameters in dry machining of the Ti6Al4V alloy was carried out. In addition, the chip morphology and chip microstructure were studied. A clear dependence of certain chip-geometric parameters on the cutting parameters studied was found. From the experimental data, several parametric relationships were developed. These relationships were able to predict the evolution of different geometric parameters as a function of cutting speed and feed, within the tested range of values. The differences between the proposed models and the experimental data were also highlighted. These parametric equations allowed quantifying the value of parameters in which the trend was clear.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/ma11071260

Cites

Statistics

Figures

Figure 1 (a) Stereoscopic optical microscopy (SOM) images of the microstructure of the tested Ti6Al4V alloy and (b) amplification and identification of α and β.
Figure 1 (a) Stereoscopic optical microscopy (SOM) images of the microstructure of the tested Ti6Al4V alloy and (b) amplification and identification of α and β.CC BY · PubMed Central
Figure 2 (a) Orthogonal cutting disposition; (b) initial machining instance; (c) sample for several cutting tests; (d) test sample design (mm); (e) relief zone design.
Figure 2 (a) Orthogonal cutting disposition; (b) initial machining instance; (c) sample for several cutting tests; (d) test sample design (mm); (e) relief zone design.CC BY · PubMed Central
Figure 3 (a) Longitudinal and (b) transverse chip section measurements.
Figure 3 (a) Longitudinal and (b) transverse chip section measurements.CC BY · PubMed Central
Figure 4 Evolution of the chip morphology as a function of cutting speed (vc) and feed rate (f).
Figure 4 Evolution of the chip morphology as a function of cutting speed (vc) and feed rate (f).CC BY · PubMed Central
Figure 5 Chip morphology for (a) vc = 30 m/min, f = 0.05 mm/r, and (b) vc = 30 m/min, f = 0.30 mm/r.
Figure 5 Chip morphology for (a) vc = 30 m/min, f = 0.05 mm/r, and (b) vc = 30 m/min, f = 0.30 mm/r.CC BY · PubMed Central
Figure 6 SOM images of the longitudinal (20×, 0.1 mm of each segment) and transverse (10×, 0.25 mm of each segment) chip sections.
Figure 6 SOM images of the longitudinal (20×, 0.1 mm of each segment) and transverse (10×, 0.25 mm of each segment) chip sections.CC BY · PubMed Central
Figure 7 Grain deformation within the chip microstructure (vc = 125 m/min, f = 0.10 mm/r).
Figure 7 Grain deformation within the chip microstructure (vc = 125 m/min, f = 0.10 mm/r).CC BY · PubMed Central
Figure 8 Chip microstructure (40×) for f = 0.10 mm/r: (a) vc = 30 m/min, and (b) vc = 125 m/min.
Figure 8 Chip microstructure (40×) for f = 0.10 mm/r: (a) vc = 30 m/min, and (b) vc = 125 m/min.CC BY · PubMed Central
Figure 9 Chip microstructure (20×) at vc = 125 m/min: (a) f = 0.20 mm/r, and (b) f = 0.30 mm/r.
Figure 9 Chip microstructure (20×) at vc = 125 m/min: (a) f = 0.20 mm/r, and (b) f = 0.30 mm/r.CC BY · PubMed Central
Figure 10 (a) Heights of peaks (hp) and (b) valleys (hv) as a function of vc and f.
Figure 10 (a) Heights of peaks (hp) and (b) valleys (hv) as a function of vc and f.CC BY · PubMed Central
Figure 11 Chip SOM images (100×) for f = 0.05 mm/r: (a) vc = 30 m/min, and (b) vc = 125 m/min.
Figure 11 Chip SOM images (100×) for f = 0.05 mm/r: (a) vc = 30 m/min, and (b) vc = 125 m/min.CC BY · PubMed Central
Figure 12 (a) Segment ratio (Gs) and (b) equivalent chip thickness (tc) as a function of vc and f.
Figure 12 (a) Segment ratio (Gs) and (b) equivalent chip thickness (tc) as a function of vc and f.CC BY · PubMed Central
Figure 13 (a) Shear angle (θ) and (b) segment width (S) as a function of vc and f.
Figure 13 (a) Shear angle (θ) and (b) segment width (S) as a function of vc and f.CC BY · PubMed Central
Figure 14 (a) Chip width (bv) and (b) chip transverse area (A) as a function of vc and f.
Figure 14 (a) Chip width (bv) and (b) chip transverse area (A) as a function of vc and f.CC BY · PubMed Central
Figure 15 Shrinkage factor (ζ) as a function of vc and f, calculated via (a) Equation (5), and (b) via Equation (2).
Figure 15 Shrinkage factor (ζ) as a function of vc and f, calculated via (a) Equation (5), and (b) via Equation (2).CC BY · PubMed Central
Figure 16 Comparisons between the experimental data and the proposed model for (a) Gs, (b) θ, and (c) ζ.
Figure 16 Comparisons between the experimental data and the proposed model for (a) Gs, (b) θ, and (c) ζ.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Still being checked.

Research works printed but not linked: 46

  1. Ezugwu, 2003, An overview of the machinability of aeroengine alloys, J. Mater. Process. Technol., 134, 233, 10.1016/S0924-0136(02)01042-7 · DOI
  2. Niknam, S.A., Khettabi, R., and Songmene, V. (2014). Machinability and Machining of Titanium Alloys: A Review. Machining of Titanium Alloys, Springer. Chapter 1. · DOI
  3. Veiga, 2013, Review on machinability of titanium alloys: The process perspective, Rev. Adv. Mater. Sci., 34, 148
  4. Arrazola, 2009, Machinability of titanium alloys (Ti6Al4V and Ti555.3), J. Mater. Process. Technol., 209, 2223, 10.1016/j.jmatprotec.2008.06.020 · DOI
  5. Llorente, 2000, Advanced cutting conditions for the milling of aeronautical alloys, J. Mater. Process. Technol., 100, 1, 10.1016/S0924-0136(99)00372-6 · DOI
  6. Boyer, 2013, The use of β titanium alloys in the aerospace industry, J. Mater. Eng. Perform., 22, 2916, 10.1007/s11665-013-0728-3 · DOI
  7. Li, 2017, Mechanical behaviors of Ti/CFRP/Ti laminates with different surface treatments of titanium sheets, Compos. Struct., 163, 21, 10.1016/j.compstruct.2016.12.033 · DOI
  8. Calamaz, 2009, A new material model for 2D numerical simulation of serrated chip formation when machining titanium alloy Ti-6Al-4V, Int. J. Mach. Tools Manuf., 48, 275, 10.1016/j.ijmachtools.2007.10.014 · DOI
  9. Nouari, M., and Makich, H. (2014). Analysis of Physical Cutting Mechanisms and Their Effects on the Tool Wear and Chip Formation Process When Machining Aeronautical Titanium Alloys: Ti-6Al-4V and Ti-55531. Machining of Titanium Alloys, Springer. · DOI
  10. Nouari, 2014, On the Physics of Machining Titanium Alloys: Interactions between Cutting Parameters, Microstructure and Tool Wear, Metals, 4, 335, 10.3390/met4030335 · DOI
  11. Cotterell, 2008, Dynamics of chip formation during orthogonal cutting of titanium alloy Ti-6Al-4V, CIRP Ann. Manuf. Technol., 57, 93, 10.1016/j.cirp.2008.03.007 · DOI
  12. Pramanik, 2014, Problems and solutions in machining of titanium alloys, Int. J. Adv. Manuf. Technol., 70, 919, 10.1007/s00170-013-5326-x · DOI
  13. Komanduri, 2002, On thermoplastic shear instability in the machining of a titanium alloy (Ti-6Al-4V), Metall. Mater. Trans. A, 33, 2995, 10.1007/s11661-002-0284-1 · DOI
  14. Wan, 2012, Microstructure evolution of adiabatic shear bands and mechanisms of saw-tooth chip formation in machining Ti6Al4V, Mater. Sci. Eng. A, 531, 155, 10.1016/j.msea.2011.10.050 · DOI
  15. Sima, 2010, Modified material constitutive models for serrated chip formation simulations and experimental validation in machining of titanium alloy Ti-6Al-4V, Int. J. Mach. Tools Manuf., 50, 943, 10.1016/j.ijmachtools.2010.08.004 · DOI
  16. Shokrani, 2012, Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids, Int. J. Mach. Tools Manuf., 57, 83, 10.1016/j.ijmachtools.2012.02.002 · DOI
  17. Kelly, 2002, Minimal lubrication machining of aluminium alloys, J. Mater. Process. Technol., 120, 327, 10.1016/S0924-0136(01)01126-8 · DOI
  18. Yoshimura, 2006, Study on near dry cutting of aluminum alloys, Mach. Sci. Technol., 10, 289, 10.1080/10910340600902108 · DOI
  19. Vinayagamoorthy, 2013, Dry machining of Ti-6Al-4V using PVD coated tools, Int. J. Appl. Eng. Res., 8, 1373
  20. Salguero, 2012, A Study of Macrogeometrical Deviations in the Dry Turning of UNS R56400 Ti Alloy, Appl. Mech. Mater., 152–154, 613, 10.4028/www.scientific.net/AMM.152-154.613 · DOI
  21. Ginting, 2009, Surface integrity of dry machined titanium alloys, Int. J. Mach. Tools Manuf., 49, 325, 10.1016/j.ijmachtools.2008.10.011 · DOI
  22. Trujillo, F.J., Sevilla, L., and Marcos, M. (2017). Experimental Parametric Model for Indirect Adhesion Wear Measurement in the Dry Turning of UNS A97075 (Al-Zn) Alloy. Materials, 10. · DOI
  23. Hua, 2004, Prediction of chip morphology and segmentation during the machining of titanium alloys, J. Mater. Process. Technol., 150, 124, 10.1016/j.jmatprotec.2004.01.028 · DOI · Paper page
  24. Molinari, 2013, Adiabatic shear banding and scaling laws in chip formation with application to cutting of Ti–6Al–4V, J. Mech. Phys. Solids, 61, 2331, 10.1016/j.jmps.2013.05.006 · DOI
  25. Liang, 2010, Experimental Studies for Burrs in Dry Drilling of Stacked Metal Materials, Adv. Mater. Res., 129–131, 959, 10.4028/www.scientific.net/AMR.129-131.959 · DOI
  26. Yang, 1999, Machining Titanium and its Alloys, Mach. Sci. Technol., 3, 107, 10.1080/10940349908945686 · DOI
  27. Calamaz, 2010, Numerical simulation of titanium alloy dry machining with a strain softening constitutive law, Mach. Sci. Technol., 14, 244, 10.1080/10910344.2010.500957 · DOI
  28. Yameogo, 2017, Prediction of the Cutting Forces and Chip Morphology When Machining the Ti6Al4V Alloy Using a Microstructural Coupled Model, Procedia CIRP, 58, 335, 10.1016/j.procir.2017.03.233 · DOI
  29. Vyas, 1999, Mechanics of saw-tooth chip formation in metal cutting, J. Manuf. Sci. Eng., 121, 163, 10.1115/1.2831200 · DOI
  30. Barry, 2001, Observations on chip formation and acoustic emission in machining Ti-6Al-4V alloy, Int. J. Mach. Tools Manuf., 41, 1055, 10.1016/S0890-6955(00)00096-1 · DOI
  31. Komanduri, 1981, New observations on the mechanism of chip formation when machining titanium alloys, Wear, 69, 179, 10.1016/0043-1648(81)90242-8 · DOI · Paper page
  32. Obikawa, 1996, Computational machining of titanium alloy—Finite element modeling and a few results, J. Manuf. Sci. Eng. Trans. ASME, 118, 208, 10.1115/1.2831013 · DOI
  33. Bai, 2017, Improved analytical prediction of chip formation in orthogonal cutting of titanium alloy Ti6Al4V, Int. J. Mech. Sci., 133, 357, 10.1016/j.ijmecsci.2017.08.054 · DOI
  34. Joshi, 2018, Dimensional inequalities in chip segments of titanium alloys, Eng. Sci. Technol. Int. J., 21, 238
  35. Turley, 1982, Calculation of shear strains in chip formation in titanium, Mater. Sci. Eng., 55, 45, 10.1016/0025-5416(82)90082-9 · DOI
  36. Sutter, 2013, Very high speed cutting of Ti-6Al-4V titanium alloy—Change in morphology and mechanism of chip formation, Int. J. Mach. Tools Manuf., 66, 37, 10.1016/j.ijmachtools.2012.11.004 · DOI
  37. Joshi, 2015, Microstructural Characterization of Chip Segmentation under Different Machining Environments in Orthogonal Machining of Ti6Al4V, J. Eng. Mater. Technol., 137, 011055, 10.1115/1.4028841 · DOI
  38. Baker, 2002, Finite Element Simulation of Segmented Chip Formation of Ti6Al4V, J. Manuf. Sci. Eng., 124, 485, 10.1115/1.1459469 · DOI
  39. Zhang, 2015, On the selection of Johnson-Cook constitutive model parameters for Ti-6Al-4V using three types of numerical models of orthogonal cutting, Procedia CIRP, 31, 112, 10.1016/j.procir.2015.03.052 · DOI
  40. Wang, 2015, Shear localization sensitivity analysis for Johnson-Cook constitutive parameters on serrated chips in high speed machining of Ti6Al4V, Simul. Model. Pract. Theory, 55, 63, 10.1016/j.simpat.2015.03.011 · DOI
  41. Ducobu, 2016, Material constitutive model and chip separation criterion influence on the modeling of Ti6Al4V machining with experimental validation in strictly orthogonal cutting condition, Int. J. Mech. Sci., 107, 136, 10.1016/j.ijmecsci.2016.01.008 · DOI
  42. Batista, 2012, Using image analysis techniques for single evaluation of the chip shrinkage factor in orthogonal cutting process, Key Eng. Mater., 504–506, 1329, 10.4028/www.scientific.net/KEM.504-506.1329 · DOI
  43. Dye, D., Hu, F., and Jones, N.G. (2011). Microstructure Formation in Alpha-Beta Titanium Alloys. Titanium, Springer.
  44. Lütjering, G., and Williams, J.C. (2007). Alpha + Beta Alloys. Titanium, Springer.
  45. Molinari, 2002, Adiabatic shear banding in high speed machining of Ti-6Al-4V: Experiments and modeling, Int. J. Plast., 18, 443, 10.1016/S0749-6419(01)00003-1 · DOI
  46. Grzesik, W. (2008). Advanced Machining Processes of Metallic Materials: Theory, Modelling and Applications, Elsevier.

Cited by

Sorted by citations

Metadata

Authors
Sánchez Hernández, Y., Trujillo Vilches, F. J., Bermudo Gamboa, C., & Sevilla Hurtado, L.
Affiliations
Department of Manufacturing Engineering, EII, Universidad de Málaga, E-29071 Málaga, Spain
ORCID
Venue
Materials
Publisher
MDPI AG
Published
2018-07-23
Volume
11
Issue
7
Pages
1260
ISSN
1996-1944 (ISSN-L)
DOI
10.3390/ma11071260
PMC
PMC6073982
OpenAlex
W2884431689
Cited by
36
References
0
Citations by year
2026: 1 · 2025: 3 · 2024: 8 · 2023: 4 · 2022: 5 · 2021: 4 · 2020: 6 · 2019: 4 · 2018: 1
FWCI
2.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Materials (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Advanced machining processes and optimization
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Machining and Optimization Techniques
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Engineering Technology and Methodologies
Keywords
Ti-6Al-4V, titanium alloys, dry machining, chip geometry, chip morphology, segmented chips, cutting speed, feed rate, parametric relationships, chip microstructure, machinability, difficult-to-cut materials

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review