Abstract
This paper presents a methodology to obtain improved quality of surface roughness during production of mobile case cover inside a cyberphysical (CP) factory using micro-CNC end milling with aluminium alloy T6 (6068). The said machining is done with different machining parameters such as cutting velocity, spindle speed and cut depth. Three profile parameters (Ra, Rz and Rzmax) are projected as response variables. Thereafter, Taguchi’s orthogonal array design is considered with smaller-is-better signal-to-noise ratio, and linear regression is performed to get optimal process parameter settings combination. This result is further verified using a particle swarm optimization (PSO) technique, and validation is done on CNC machining centre.
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References
Ghosh T, Wang Y, Martinsen K, Wang K (2020) A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105. Int J Adv Manuf Technol 111:2419–2439
Rajeswari B, Amirthagadeswaran K (2017) Experimental investigation of machinability characteristics and multi-response optimization of end milling in aluminium composites using RSM based grey relational analysis. Measurement 105:78–86
Hu L (2014) CNC milling of complex aluminum parts. Lehigh Preserve Institutional Repository
Krolczyk GM, Legutko S (2017) Experimental analysis by measurement of surface roughness variations in turning process of duplex stainless steel. Metrol Meas Syst 21(4):759–770
Pillaia JU, Sanghrajkaa I, Shunmugavel M, Muthuramalingam T, Goldberg M, Littlefair G (2018) Optimisation of multiple response characteristics on end milling of aluminium alloy using Taguchi-Grey relational approach. Measurement 124:291–298
Okokpujie IP, Ajayi OO, Afolalu SA, Abioye AA, Salawu EY, Udo MO (2018) Modeling and optimization of surface roughness in end milling of aluminium using least square approximation method and response surface methodology. Int J Mech Eng Technol 9(1):587–600
Kumar G, Kumar M, Tomer A (2020) Optimization of end milling machining parameters of SS 304 by Taguchi technique. In: Muzammil M, Chandra A, Kankar PK, Kumar H (eds) Lecture notes in mechanical engineering. Springer, Singapore
Qehaja N, Zhujani F, Abdullahu F (2018) Mathematical model determination for surface roughness during CNC end milling operation on 42CRMO4 hardened steel. Int J Mech Eng Technol 9(1):624–632
Wojciechowski S, Wiackiewicz M, Krolczyk GM (2018) Study on metrological relations between instant tool displacements and surface roughness during precise ball end milling. Measurement 129:686–694
Li Z-L, Zhu L (2016) Mechanistic modeling of five-axis machining with a flat end mill considering bottom edge cutting effect. J Manuf Sci Eng 138:111012
Gao P, Liang Z, Wang X, Li S, Zhou T (2018) Effects of different chamfered cutting edges of micro end mill on cutting performance. Int J Adv Manuf Technol 96:1215–1224
Das R, Mohanty SS, Panigrahi M, Mohanty S (2018) Predictive modelling and analysis of surface roughness in CNC milling of green alumina using response surface method and genetic algorithm. In: IOP conference series: materials science and engineering, vol 410
MakeItFrom, Home>Aluminum Alloy>AA 6000 Series (Aluminum-Magnesium-Silicon Wrought Alloy)>6082 Aluminum [Online]. Available https://www.makeitfrom.com/material-properties/6082-T6-Aluminum. Accessed 25 Nov ember 2021
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science
Ghosh T, Martinsen K (2020) CFNN-PSO: an iterative predictive model for generic parametric design of machining processes. Appl Artif Intell 33(11):951–978
Acknowledgements
This work is supported by the SFI Manufacturing (Project No. 237900) and funded by the Research Council of Norway (RCN).
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Kaushik, Y., Ghosh, T. (2023). PSO-Based Improved Surface Roughness Measuring Approach of Manufactured Product Within CP Factory Using T6 6068 Aluminium. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 465. Springer, Singapore. https://doi.org/10.1007/978-981-19-2397-5_16
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DOI: https://doi.org/10.1007/978-981-19-2397-5_16
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