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Misorientation Angle Study on Cleavage Fracture Propagation Surfaces of a Grade a Ship Steel, through Charpy and Four-Point Double-Notch Bend Tests
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Numerical Study of Drilling Parameters with Al 356 Alloy Using Bacterial Foraging Optimization
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Numerical Study of Drilling Parameters with Al 356 Alloy Using Bacterial Foraging Optimization

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Abstract:

A mechanical work piece created industrially frequently contains more than one machining process. Furthermore, it is a common activity of programmers, who make this selection every time a milling and drilling operation is conducted. Tool wear and borehole quality are two essential challenges for high precision drilling procedures, with Al 356 alloy being employed in experimental planning. Drilling specifications will be assessed in this work to get optimal parameters in minimizing the influence of drilling damage on alloy using a swarm-based optimization model. The drilling parameters are optimized using the Bacterial Foraging Optimization (BFO) method, which includes three control factors: depth, feed rate, and spindle speed. Each parameter is designed in three levels, with multiple performance characteristics such as thrust force, surface roughness, and delamination factor. This investigation was carried out in order to obtain the proper optimization. The feed rate, next to the spindle speed, was discovered to be the essential element inducing lamination in drilling, with this phenomenon occurring in each diameter of the drill bit. The results reveal that the feed rate and drill type are the most important parameters influencing the drilling process, and that employing this strategy can successfully improve drilling process outcomes.

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Periodical:

Materials Science Forum (Volume 1121)

Pages:

23-34

DOI:

https://doi.org/10.4028/p-6hUKkB

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Online since:

May 2024

Authors:

Pandian Pitchipoo*, Muthiah Athi, Manikandan Annamalai, Jeyakumar Kadarkarai, Rajakarunakaran Sivaprakasam

Keywords:

Drilling, Machining Parameters, Optimization, Roughness, Spindle Speed, Thrust Force

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© 2024 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] T. Rajmohan, K. Palanikumar, M. Kathirvel, Optimization of machining parameters in drilling hybrid aluminium metal matrix composites, T. Nonferr. Metal Soc. 22 (6) (2012) 1286-1297

DOI: 10.1016/S1003-6326(11)61317-4

Google Scholar

[2] V. Krishnaraj, R. Sindhumathi, A. Prabukarthi, S. Harichealvan, Multi objective optimisation of machining parameters during high speed drilling of Ti6Al4V alloy under dry condition, Int. J. Mater. Eng. Innov. 9 (3) (2018) 195-207

DOI: 10.1504/IJMATEI.2018.096042

Google Scholar

[3] U.A. Khashaba, I.A. El-Sonbaty, A.I. Selmy, A.A. Megahed, Machinability analysis in drilling woven GFR/epoxy composites: Part I–Effect of machining parameters, Compos. - A: Appl. Sci. Manuf. 41 (3) (2010) 391-400

DOI: 10.1016/j.compositesa.2009.11.006

Google Scholar

[4] Patwari, Md Anayet U., SM Tawfiq Ullah, Ragib Ishraq Khan, Md Mahfujur Rahman, Prediction and optimization of surface roughness by coupled statistical and desirability analysis in drilling of mild steel, Ann. Fac. Eng. Hunedoara. Int. J. Eng. 11 (2) (2013): 161-166.

Google Scholar

[5] Jogendra Kumar, Sujay Majumder, Arpan Kumar Mondal, Rajesh Kumar Verma, Influence of rotation speed, transverse speed, and pin length during underwater friction stir welding (UW-FSW) on aluminum AA6063: A novel criterion for parametric control, Int. J. Lightweight Mater. Manuf. 5 (3) (2022) 295-305.

DOI: 10.1016/j.ijlmm.2022.03.001

Google Scholar

[6] Minquiz, Gustavo M., Vicente Borja, Marcelo Lopez-Parra, Alejandro C. Ramirez-Reivich, Leopoldo Ruiz-Huerta, R. C. Lazaro, Alejandro Shigeru Yamamoto Sánchez, H. Vazquez-Leal, Maria-Esther Pavon-Solana, J. Flores Mendez, Machining parameters and toolpath productivity optimization using a factorial design and fit regression model in face milling and drilling operations, Math. Probl. Eng. 2020 (2020) 8718597

DOI: 10.1155/2020/8718597

Google Scholar

[7] Vijayan Krishnaraj, A. Prabukarthi, Arun Ramanathan, N. Elanghovan, M. Senthil Kumar, Redouane Zitoune, J.P. Davim, Optimization of machining parameters at high speed drilling of carbon fiber reinforced plastic (CFRP) laminates, Compos. B: Eng. 43 (4) (2012) 1791-1799.

DOI: 10.1016/j.compositesb.2012.01.007

Google Scholar

[8] S. Aravind, K. Shunmugesh, Jibin Biju, Joshua K. Vijayan, Optimization of micro-drilling parameters by Taguchi grey relational analysis, Mater. Today Proc. 4 (2) (2017) 4188-4195

DOI: 10.1016/j.matpr.2017.02.121

Google Scholar

[9] A. N. Haq, P. Marimuthu, R. Jeyapaul, Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method, Int. J. Adv. Manuf. Technol. 37 (2008) 250-255

DOI: 10.1007/s00170-007-0981-4

Google Scholar

[10] Abhishek Sharma, Neeraj Sharma, Ravinder Pal Singh, Rishu Arora, Randeep Singh Gill, Gurminder Singh, Micro-drill on Al/SiC composite by EDD process: An RSM-MOGOA based hybrid approach, Int. J. Lightweight Mater. Manuf. 5 (4) (2022) 564-575.

DOI: 10.1016/j.ijlmm.2022.07.002

Google Scholar

[11] S. Devaraj, Ramakrishna Malkapuram, B. Singaravel, Performance analysis of micro textured cutting insert design parameters on machining of Al-MMC in turning process, Int. J. Lightweight Mater. Manuf. 4 (2) (2021) 210-217.

DOI: 10.1016/j.ijlmm.2020.11.003

Google Scholar

[12] P. Pitchipoo, A. Muthiah, K. Jeyakumar, A. Manikandan, Friction stir welding parameter optimization using novel multi objective dragonfly algorithm, Int. J. Lightweight Mater. Manuf. 4 (4) (2021) 460-467.

DOI: 10.1016/j.ijlmm.2021.06.006

Google Scholar

[13] Gaurav Chaudhary, Manoj Kumar, Santosh Verma, Anupam Srivastav, Optimization of drilling parameters of hybrid metal matrix composites using response surface methodology, Procedia Mater. Sci. 6 (2014) 229-237.

DOI: 10.1016/j.mspro.2014.07.028

Google Scholar

[14] Z. Mustafa, N.H. Idrus, A B. Mohd Hadzley, D. Sivakumar, M.Y. Norazlina, S.H.S.M. Fadzullah, A. Anjang, K. Thongkaew, Optimization of drilling process parameters on delamination factor of Jute reinforced unsaturated polyester composite using Box-Behnken design of experiment, J. Mech. Eng. Sci. 14 (1) (2020) 6295-6303

DOI: 10.15282/jmes.14.1.2020.08.0493

Google Scholar

[15] B. V. Ramnath, S. Sharavanan, J. Jeykrishnan, Optimization of process parameters in drilling of fibre hybrid composite using Taguchi and grey relational analysis, IOP Conf. Ser.: Mater. Sci. Eng. 183 (1) (2017) 012003

DOI: 10.1088/1757-899X/183/1/012003

Google Scholar

[16] Babu, S. Senthil, C. Dhanasekaran, G. Anbuchezhiyan, Kumaran Palani, Parametric analysis on drilling of aluminium alloy hybrid composites reinforced with SIC/WC, Eng. Res. Exp. 4 (2) (2022) 025036

DOI: 10.1088/2631-8695/ac7038

Google Scholar

[17] Ahamed, Ashfaq, Athif Ahamed, Dilan Katuwawala, Teoh Tiong Ee, Zi Han Tan, Ishwin Singh Bajaj, Thejan Wickramasurendra, Hamidreza Namazi, Complexity-based analysis of the influence of machining parameters on the surface finish of drilled holes in drilling operation, Fractals, 27 (06) (2019) 1950087

DOI: 10.1142/S0218348X19500877

Google Scholar

[18] Pawanr, Shailendra, Girish Kant Garg, Srikanta Routroy, Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS, Procedia CIRP 86 (2019) 116-120

DOI: 10.1016/j.procir.2020.01.036

Google Scholar

[19] X. Wang, R. Eisseler, H-C. Moehring. Prediction and optimization of machining results and parameters in drilling by using Bayesian networks, Prod. Eng. Res. Devel. 14 (3) (2020) 373-383

DOI: 10.1007/s11740-020-00965-w

Google Scholar

[20] S. Chandrabakty, I. Renreng, Z. Djafar, H. Arsyad, An optimization of the machining parameters on delamination in drilling ramie woven reinforced composites using Taguchi method, J. Phys. Conf. Ser, 1341 (5) (2019) 052005

DOI: 10.1088/1742-6596/1341/5/052005

Google Scholar

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