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Performance analysis of developed micro-textured cutting tool in machining aluminum alloy 7075-T6: assessment of tool wear and surface roughness

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Abstract

Due to the wide applications of aluminum alloy in numerous industrial sectors and products, significant attention has been paid by many researchers to improve the production and manufacturing aspects of aluminium alloys and related products. One of the main concerns in this regard is the cutting tool life when machining aluminium alloys. This work aims to present the capability and performance analysis of a new strategy of life improvement in the tungsten carbide inserts by creating micro-textured grooves on their surface. For this purpose, the laser was used to create the different morphologies of textures on the insert surfaces. The grooves were created with different distances in parallel to the cutting edge on the tool rake face. The effects of fabricated grooves on the tool wear attributes were discussed in three directions, including the main edge (x-direction), side edge (y-direction), and radial (in the direction of 45°), and the effect of these grooves/textures on the surface roughness attributes was also studied. To that end, experimental works were performed under dry and minimum quantity lubrication (MQL), as well as three levels of cutting speed, feed, and grooves distance. The experimental results were compared with those measured under non-textured inserts. Finally, the obtained results were statistically analyzed, and the empirical models were formulated. Experimental results indicated that micro-textured inserts in both MQL and dry conditions led to surface quality and tool wear as compared to readings made under non-textured inserts.

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The authors confirm that the data supporting the findings of this study are available within the article (and/or) its supplementary materials.

References

  1. Rodrigues SP, Alves CF, Cavaleiro A, Carvalho S (2017) Water and oil wettability of anodized 6016 aluminum alloy surface. Appl Surf Sci 422:430–442

    Article  Google Scholar 

  2. Musavi SH, Davoodi B, Niknam SA (2018) Environmental-friendly turning of A286 superalloy. J Manuf Process 32:734–743

    Article  Google Scholar 

  3. Orra K, Choudhury SK (2018) Tribological aspects of various geometrically shaped micro-textures on cutting insert to improve tool life in hard turning process. J Manuf Process 31:502–513

    Article  Google Scholar 

  4. Gomez-Parra A, Alvarez-Alco M, Salguero J, Batista M, Marcos M (2013) Analysis of the evolution of the built-up edge and built-up layer formationmechanisms in the dry turning of aeronautical aluminum alloys. Wear 302:1209–1218

    Article  Google Scholar 

  5. Martinez I, Tanaka R, Yamane Y, Sekiya K, Yamada K, Ishihara T, Furuya S (2016) Wear mechanism of coated tools in the turning of ductile cast iron having awide range of tensile strength. Precis Eng 47:46–53

    Article  Google Scholar 

  6. Kannan S, Ghosh A (2018) Anti-friction and wetting behavior of a new polymer composite coating towards aluminum and dry machining of AA2024 alloy by coated end mills. J Mater Process Technol 252:280–293

    Article  Google Scholar 

  7. Musavi SH, Davoodi B (2021) Risk assessment for hazardous lubricants in machining industry. Environ Sci Pollut Res 28:625–634. https://doi.org/10.1007/s11356-020-10472-1

    Article  Google Scholar 

  8. Musavi SH, Davoodi B, Nankali M (2021) Assessment of tool wear and surface integrity in ductile cutting using a developed tool. Arab J Sci Eng. https://doi.org/10.1007/s13369-021-05560-4

    Article  Google Scholar 

  9. Singh R, Dureja JS, Dogra M et al (2020) Wear behavior of textured tools under graphene-assisted minimum quantity lubrication system in machining Ti-6Al-4V alloy. Tribol Int 145:106–183

    Google Scholar 

  10. Arulkirubakaran D, Senthilkumar V, Kumawat V (2016) Effect of micro-textured tools on machining of Ti–6Al–4V alloy: an experimental and numerical approach. Int J Refract Hard Me 54:165–177

    Article  Google Scholar 

  11. Niketh S, Samuel GL (2017) Surface texturing for tribology enhancement and its application on drill tool for the sustainable machining of titanium alloy. J Clean Prod 167:253–270

    Article  Google Scholar 

  12. Obikawa T, Kamio A, Takaoka H, Osad A (2011) Micro-texture at the coated tool face for high-performance cutting. Int J Mach Tools Manuf 51:966–972

    Article  Google Scholar 

  13. Musavi SH, Davoodi B, Eskandari B (2019) Pre-cooling intensity effects on cooling efficiency in cryogenic turning. Arab J Sci Eng 44:10389–10396. https://doi.org/10.1007/s13369-019-04056-6

    Article  Google Scholar 

  14. Feng Y, Zhang J et al (2017) Fabrication techniques and cutting performance of micro-textured self-lubricating ceramic cutting tools by in-situ forming of Al2O3-TiC. Int J Refract Metal Hard Mater 68:121–129

    Article  Google Scholar 

  15. Kim DM, Lee I, Kim SK, Kim BH, Park HW (2016) Influence of a micropatterned insert on characteristics of the tool–workpiece interface in a hard turning process. J Mater Process Technol 229(2016):160–171

    Article  Google Scholar 

  16. Zhang K, Deng J, Sun J, Jiang Ch, Liu Y, Chen Sh (2015) Effect of micro/nano-scale textures on anti-adhesive wear propertiesof WC/Co-based TiAlN coated tools in AISI 316 austenitic stainless steel cutting. Appl Surf Sci 355:602–614

    Article  Google Scholar 

  17. Liu X, Liu Y, Li L, Tian Y (2019) Performances of micro-textured WC-10Ni3Al cemented carbides cutting tool in turning of Ti6Al4V. Int J Refract Metals Hard Mater 84:104987

  18. Kummel J, Braun D, Gibmeier J, Schneider J, Greiner Ch, Schulze V, Wanner A (2015) study on micro-texturing of uncoated cemented carbide cutting tools for wear improvement and built-up edge stabilization 215:62–70

  19. Fang Z, Obikawa T (2017) Cooling performance of micro-texture at the tool flank face under high-pressure jet coolant assistance. Precis Eng 49:41–51

    Article  Google Scholar 

  20. Arulkirubakaran D, Senthilkumar V, Chilamwar VL, Senthil P (2019) Performance of surface textured tools during machining of Al-Cu/TiB2 composite. Measurement 137:636–646

    Article  Google Scholar 

  21. Musavi SH, Davoodi B, Eskandari B (2020) Evaluation of surface roughness and optimization of cutting parameters in turning of AA2024 alloy under different cooling-lubrication conditions using RSM method. J Cent South Univ 27(6):1714–1728

    Article  Google Scholar 

  22. Natarajan U, Periyanan P, Yang S (2011) Multiple-response optimization for micro-endmilling process using response surface methodology. Int J Adv Manuf Technol 56:177–185

    Article  Google Scholar 

  23. Davoodi B, Eskandari B (2015) Tool wear mechanisms and multi-response optimization of tool life and volume of material removed in turning of N-155 iron-nickel-base superalloy using RSM. Measurement 68:286–294

    Article  Google Scholar 

  24. Montgomery DC (1984) Design and analysis of experiments. Wiley, New York

    Google Scholar 

  25. Davoodi B, Musavi SH (2016) An experimental investigation of the effect of lubrication method on surface roughness and cutting fluid consumption in machining of super alloys. Mod Mech Eng 16:343–352

    Google Scholar 

  26. Musavi SH, Davoodi B, Niknam SA (2019) Effects of reinforced nanofluid with nanoparticles on the cutting tool wear morphology. J Cent South Univ 26(5):1050–1064

    Article  Google Scholar 

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Contributions

The research results in this work were presented in the M.S thesis of Mr. Sepehrikia. Mr. Musavi, the current Ph.D. student, helped him in experimental works and data analysis. Dr. Niknam and Dr.Davoodi also acted as the supervisors of Mr. Sepehrikia. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Seyed Ali Niknam.

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Appendix

Appendix

Tables 12 and 13 show the values of measured tool wear in different directions using dry and MQL machining modes.

Table 12 Matrix L20 of response surface methodology of wear results in the dry method using the textured tool
Table 13 Matrix of response surface methodology of wear results using conventional tool

The final tool wear models in different directions for dry machining with the textured tool, MQL machining with the conventional tool, and dry machining with the conventional tool are presented in Eqs. (7) to (9), Eqs. (10) to (12), and Eqs. (13) to (15), respectively:

$$\begin{aligned}TWx&=1.257-0.008{V}_{c}+0.68545f-0.0011d-0.008{V}_{c}\kern 0.1500emf\\&-0.000002{V}_{c}d+0.000fd+0.0000247{{V}_{c}}^{2}\\&+4.18182{f}^{2}+0.00000605{d}^{2}\end{aligned}$$
(7)
$$\begin{aligned}TWy&=1.246-0.0092{V}_{c}+1.54f-0.00116d-0.008{V}_{c}\kern 0.1500emf\\&-0.000001{V}_{c}d+0.0005fd+0.000032{{V}_{c}}^{2}+0.000{f}^{2}\\&+0.0000055{d}^{2}\end{aligned}$$
(8)
$$\begin{aligned}TWr&=0.292+0.0042{V}_{c}-0.458f-0.0014d\\&-0.011{V}_{c}\kern 0.1500emf+0.0000005{V}_{c}d+0.00325fd\\&-0.0000138{{V}_{c}}^{2}+6.5454{f}^{2}+0.0000041{d}^{2}\end{aligned}$$
(9)
$$\begin{aligned}TWx&=-0.26362+0.013701{V}_{c}+4.75557f\\&-0.0134{V}_{c}\kern 0.1500emf+0.00006209{{V}_{c}}^{2}-4.72414{f}^{2}\end{aligned}$$
(10)
$$\begin{aligned}TWy&=-3.84953+0.13122{V}_{c}+31.78626f\\&-0.0622{V}_{c}\kern 0.1500emf-0.0005935{{V}_{c}}^{2}-39.7931{f}^{2}\end{aligned}$$
(11)
$$\begin{aligned}TWr&=2.34057-0.02034{V}_{c}+0.77931f\\&+0.000{V}_{c}\kern 0.1500emf+0.000056{{V}_{c}}^{2}+2.06897{f}^{2}\end{aligned}$$
(12)
$$\begin{aligned}TWx&=3.1046-0.025485{V}_{c}-0.83218f+0.008{V}_{c}\kern 0.1500emf\\&+0.0000662{{V}_{c}}^{2}+4.55172{f}^{2}\end{aligned}$$
(13)
$$\begin{aligned}TWy&=2.53017-0.017221{V}_{c}-1.25287f+0.028{V}_{c}\kern 0.1500emf\\&+0.00002648{{V}_{c}}^{2}-3.37931{f}^{2}\end{aligned}$$
(14)
$$\begin{aligned}TWr&=5.44098-0.06825{V}_{c}-1.0885f+0.036{V}_{c}\kern 0.1500emf\\&+0.00023{{V}_{c}}^{2}-6.48276{f}^{2}\end{aligned}$$
(15)

Tables 14 and 15 show the values of measured surface roughness in different machining modes

Table 14 Matrix L20 of response surface methodology of roughness results in dry method using textured tool
Table 15 Matrix of response surface methodology of roughness results using conventional tool

The final roughness models for MQL machining with textured tool, dry machining with textured tool, MQL machining with conventional tool, and dry machining with conventional tool are presented in Eqs. (16) to (19), respectively:

$$\begin{aligned}{R}_{\mathrm{a}}&=1.047-0.0129{V}_{c}+3.909f-0.00096d-0.013{V}_{c}\kern 0.1500emf\\&-0.0000066{V}_{c}d-0.0014fd+0.000053{{V}_{c}}^{2}-3.1818{f}^{2}\\&+0.0000078{d}^{2}\end{aligned}$$
(16)
$$\begin{aligned}{R}_{\mathrm{a}}&=0.8221-0.015{V}_{c}+9.9377f-0.0004d\\&-0.0158{V}_{c}\kern 0.1500emf-0.000006{V}_{c}d-0.0013fd\\&+0.000065{{V}_{c}}^{2}-19.10909{f}^{2}+0.00000622{d}^{2}\end{aligned}$$
(17)
$$\begin{aligned}{R}_{\mathrm{a}}&=-0.26362+0.013701{V}_{c}+4.75557f-0.0134{V}_{c}\kern 0.1500emf\\&+0.00006209{{V}_{c}}^{2}-4.72414{f}^{2}\end{aligned}$$
(18)
$$\begin{aligned}{R}_{\mathrm{a}}&=2.16109-0.0110781{V}_{c}-2.73931f-0.0072{V}_{c}\kern 0.1500emf\\&+0.00002692{{V}_{c}}^{2}+27.13103{f}^{2}\end{aligned}$$
(19)

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Musavi, S., Sepehrikia, M., Davoodi, B. et al. Performance analysis of developed micro-textured cutting tool in machining aluminum alloy 7075-T6: assessment of tool wear and surface roughness. Int J Adv Manuf Technol 119, 3343–3362 (2022). https://doi.org/10.1007/s00170-021-08349-9

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