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Keywords

AA2024, Turning machine, S/N ratio, Surface Roughness, Taguchi technique, Analysis of Variance

Document Type

Research Paper

Abstract

Surface roughness is a critical parameter that profoundly influences the functionality and performance of materials and components across various industries. Additionally, estimating the ideal machining parameter circumstances is the best way to minimize costs and get the item's surface quality. This research investigated four factors- depth of cut, spindle speed, feed, and nose radius- for the turning of aluminum alloy AA2024 to estimate the surface roughness. The study's experimental efforts were done by utilizing a manual turning machine, and the experiment setup used the Taguchi method of L18 array orthogonal (OA). The Taguchi approach is employed to optimize the selection of tools. The average surface roughness (Ra) measurements were converted to signal-to-noise (S/N) ratios and analyzed statistically using the analysis of variance (ANOVA) method. The results illustrated that nose radius, feed, speed, and depth of cut that produced the best results were 0.8, 0.066 mm/min, 1100 rpm, and 0.9 mm, respectively. The feed rate contributed the most with 54.51%, while the nose radius had the smallest impact with 0.84% of the percentage contribution. A2/B3/C1/D2 is the most efficient arrangement for lathe parameters. Among the selected components, only the feed rate showed a significant P-value of 0.006. Surface roughness offers numerous benefits for AA2024, an aluminum alloy widely utilized in aerospace applications. Enhanced surface roughness fosters better adhesion of coatings and adhesives and is crucial for strong bonds in aircraft components.

References

K. D. Narooei and R. Ramli, Optimal selection of cutting parameters for surface roughness in milling machining of AA6061-T6, Int. J. Eng., 35 (2022) 1170-1177. https://doi.org/10.5829/ije.2022.35.06c.08 A. H. Abdelrazek, I. A. Choudhury, Y. Nukman and S. N. Kazi, Metal cutting lubricants and cutting tools: a review on the performance improvement and sustainability assessment, Int. J. Adv. Manuf. Technol., 106 (2020) 4221-4245. https://doi.org/10.1007/s00170-019-04890-w M. Ostapenko and D. S. Vasilega, Method of evaluation of quality of metal-cutting tool, Appl. Mech. Mater., 379 (2013) 49-55. https://doi.org/10.4028/www.scientific.net/AMM.379.49 S. G. Hussein, An experimental study of the effects of coolant fluid on surface roughness in turning operation for brass alloy, J. Eng., 20 (2014) 96-104. https://doi.org/10.31026/j.eng.2014.03.09 I. Mukherjee and P. K. Ray, A review of optimization techniques in metal cutting processes, Comput. Ind. Eng., 50 (2006) 15-34. http://doi.org/10.1016/j.cie.2005.10.001 Y. Altintaş, Direct adaptive control of end milling process, Int. J. Adv. Manuf. Technol., 34 (1994) 461-472. https://doi.org/10.1016/0890-6955(94)90078-7 T. Lee and Y. Lin, A 3D predictive cutting-force model for end milling of parts having sculptured surfaces, Int. J. Adv. Manuf. Technol., 16 (2000) 773-783. https://doi.org/10.1007/s001700070011 H. H. Abdulridha, A. J. Helael and A. A. Al-duroobi, Prediction the Influence of Machining Parameters for CNC Turning of Aluminum Alloy Using RSM and ANN, Eng. Technol. J., 38 (2020) 887-895. https://doi.org/10.30684/etj.v38i6A.705 Y.-H. Tsai, J. C. Chen and S.-J. Lou, An in-process surface recognition system based on neural networks in end milling cutting operations, Int. J. Mach. Tools Manuf., 39 (1999) 583-605. https://doi.org/10.1016/S0890-6955(98)00053-4 K. Kadirgama, M. Noor and M. Rahman, Optimization of surface roughness in end milling using potential support vector machine, Arabian J. Sci. Eng., 37 (2012) 2269-2275. https://doi.org/10.1007/s13369-012-0314-2 P. Kah, C. Vimalraj, J. Martikainen and R. Suoranta, Factors influencing Al-Cu weld properties by intermetallic compound formation, Int. J. Mech. Mater. Eng., 10 (2015) 1-13. https://doi.org/10.1186/s40712-015-0037-8 A. M. Khorasani, M.R.S. Yazdi and M.S. Safizadeh, Analysis of machining parameters effects on surface roughness: a review, Int. J. Comput. Mater. Sci. Surf. Eng., 5 (2012) 68-84. https://doi.org/10.1504/IJCMSSE.2012.049055 C. Nouveau, E. Jorand, C. Decès-Petit, C. Labidi and M.-A. Djouadi, Influence of carbide substrates on tribological properties of chromium and chromium nitride coatings: application to wood machining, Wear, 258 (2004) 157-165. https://doi.org/10.1016/j.wear.2004.09.034 N. S. Patel, P.L. Parihar and J.S. Makwana, Parametric optimization to improve the machining process by using Taguchi method: a review, Mater. Today: Proc., 47 (2021) 2709-2714. https://doi.org/10.1016/j.matpr.2021.03.005 J. Ribeiro, H. Lopes, L. Queijo and D. Figueiredo, Optimization of cutting parameters to minimize the surface roughness in the end milling process using the Taguchi method, Period. Polytech., Mech. Eng., 61 (2017) 30-35. http://dx.doi.org/10.3311/PPme.9114 J. L. Rosa, A. Robin, M. B. Silva, C. A. Baldan and M. P. Peres, Electrodeposition of copper on titanium wires: Taguchi experimental design approach, J. Mater. Process. Technol., 209 (2009) 1181-1188. https://doi.org/10.1016/j.jmatprotec.2008.03.021 Selden, P. H., Sales Process Engineering. In Personal Workshop (Milwaukee: ASQ Quality Press, 1997. N. Q. Mahmood, Y. F. Tahir, M. Hikmat, M. S. Abdulsatar and P. Baumli, Experimental investigation of the surface roughness for aluminum alloy AA6061 in milling operation by taguchi method with the anova technique, J. Eng., 30 (2024) 1-14. https://doi.org/10.31026/j.eng.2024.03.01 Q. M.D. Al Attaby, M .H. Al Saadi and I. K. A. Al Naim, Improvement of Resistance Spot Welding by Surfaces Treatment of AA1050 Sheets, J. Eng., 19 (2013) 217-234. https://doi.org/10.31026/j.eng.2013.02.05 M. S. Faris, Study of the Pitting Corrosion for shot peening 6061-T6 Aluminum Alloy in sea water, Iraq. j. Mech. Mater. Eng., 17 (2017). V. A. Rogov and G. Siamak, Optimization of surface roughness and vibration in turning of aluminum alloy AA2024 using taguchi technique, Int. J. Mech. Mechatron. Eng., 7 (2014) 2330-2339.‏ M. Abas, L. Sayd, R. Akhtar, Q. S. Khalid, A. M. Khan and C. I. Pruncu, Optimization of machining parameters of aluminum alloy 6026-T9 under MQL-assisted turning process, J. Mater. Res. Technol., 9 (2020) 10916-10940.‏ https://doi.org/10.1016/j.jmrt.2020.07.071 M. Javidikia, M. Sadeghifar, V. Songmene, M. Jahazi, Analysis and optimization of surface roughness in turning of AA6061-T6 under various environments and parameters, Procedia CIRP, 101 (2021) 17-20. https://doi.org/10.1016/j.procir.2021.02.004 J. Joel and A. Xavior, Optimization on machining parameters of aluminium alloy hybrid composite using carbide insert, Mater. Res. Express, 6 (2019) 116532. http://dx.doi.org/10.1088/2053-1591/ab46c7 P. Sahoo, A. Pratap and A. Bandyopadhyay, Modeling and optimization of surface roughness and tool vibration in CNC turning of Aluminum alloy using hybrid RSM-WPCA methodology, Int. J. Ind. Eng. Comput., 8 (2017) 385-398.‏ http://dx.doi.org/10.5267/j.ijiec.2016.11.003

Highlights

Surface roughness improved both cost and quality. Feed rate showed the largest percentage contribution at 54.51%. Feed rate was the only factor with a significant P-value of 0.006. The optimal levels were a DOC of 0.9 mm, a feed of 0.066 mm/min, a speed of 1100 rpm, and a tool nose radius of 0.8.

DOI

10.30684/etj.2024.152199.1797

First Page

1474

Last Page

1483

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