Study of surface roughness variability in end milling of Al7136 alloy using row wise statistics analysis
Aurel Mihail TITU, Alina Bianca POP
Abstract. This study aims to investigate the surface roughness (Ra) of machined surfaces using a dataset comprising 150 experiments. By analyzing the influence of key machining parameters—cutting speed, depth of cut, and feed per tooth—on Ra, this research seeks to optimize the machining process for achieving superior surface finishes. The methodological challenge lies in effectively analyzing the dataset, which involves calculating summary statistics, including means, standard errors, and confidence intervals, for all 150 records. The results indicate an average value of surface roughness around 165.68 µm, with a standard error of approximately 164.661 µm, highlighting the significant impact of machining parameters on these values. The conclusions suggest that optimizing machining parameters can lead to improved quality of machined surfaces and, consequently, reduced production costs, emphasizing the need for further research to explore the relationship and propose efficient methods for optimizing the machining process.
Keywords
Surface Roughness Variability, End Milling Process, Al7136, Row Wise Statistics Analysis, Machining Optimization
Published online 12/10/2024, 8 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Aurel Mihail TITU, Alina Bianca POP, Study of surface roughness variability in end milling of Al7136 alloy using row wise statistics analysis, Materials Research Proceedings, Vol. 46, pp 143-150, 2024
DOI: https://doi.org/10.21741/9781644903377-19
The article was published as article 19 of the book Innovative Manufacturing Engineering and Energy
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] G. Basar, H. Kirli Akin, F. Kahraman, Y. Fedai, Modeling and optimization of face milling process parameters for AISI 4140 steel, Tehnički glasnik 12 (2018) 5-10. https://doi.org/10.31803/tg-20180201124648
[2] T.M. Damesa, J. Möhring, M. Worku, H.P. Piepho, One step at a time: Stage‐wise analysis of a series of experiments, Agron. J. 109 (2017) 845-857. https://doi.org/10.2134/agronj2016.07.0395
[3] M. Özdemir, A. Şahinoğlu, M. Rafighi, V. Yilmaz, Analysis and optimisation of the cutting parameters based on machinability factors in turning AISI 4140 steel, Can. Metall. Q. 61 (2022) 407-417. https://doi.org/10.1080/00084433.2022.2058154
[4] V. Vinoth, Optimization of process parameters in turning of aluminum alloy using response surface methodology, Mater. Today: Proc. 46 (2021) 9462-9468. https://doi.org/10.1016/j.matpr.2020.03.236
[5] H.D. Kumar, S. Ilangovan, N. Radhika, Optimization of cutting parameters for MRR, tool wear and surface roughness characteristics in machining ADC12 piston alloy using DOE, Tribol. Ind. 42 (2020) 32. https://doi.org/10.24874/ti.2020.42.01.03
[6] S. Dutta, S.K.R. Narala, Optimizing turning parameters in the machining of AM alloy using Taguchi methodology, Measurement 169 (2021) 108340. https://doi.org/10.1016/j.measurement.2020.108340
[7] R.K. Rao, S.C. Kulkarni, S. Shivakumar, Investigation Of Machinability Characteristics Of Sae Ams4413b Aluminum Alloy Using Ethylene Glycol As A Coolant, Migration Lett. 21 (2024) 893-932. https://doi.org/10.59670/ml.v20i8.7139
[8] A. Sudianto, Z. Jamaludin, A.A.A. Rahman, Prediction of surface roughness for development of smart milling machine, J. Phys. Conf. Ser. 1201 (2019) 012008. https://doi.org/10.1088/1742-6596/1201/1/012008
[9] A.M. Țîțu, A.V. Sandu, A.B. Pop, Ș. Țîțu, D.N. Frățilă, C. Ceocea, A. Boroiu, Design of experiment in the milling process of aluminum alloys in the aerospace industry, Appl. Sci. 10 (2020) 6951. https://doi.org/10.3390/app10196951
[10] A.B. Pop, A.M. Titu, Verification of the random nature of the experimental data in the end-milling process of aluminum alloys, Arch. Metall. Mater. (2023) 967-971. https://doi.org/10.24425/amm.2023.145461
[11] A.B. Pop, M.A. Ţîţu, G.I. Pop, Ş. Ţîţu, Modeling the machined surface quality of an aluminum alloy using the active experiment type, IOP Conf. Ser. Mater. Sci. Eng. 572 (2019) 012043. https://doi.org/10.1088/1757-899X/572/1/012043
[12] M. Aamir, K. Giasin, M. Tolouei-Rad, A. Vafadar, A review: Drilling performance and hole quality of aluminium alloys for aerospace applications, J. Mater. Res. Technol. 9 (2020) 12484-12500. https://doi.org/10.1016/j.jmrt.2020.09.003
[13] M. Hourmand, A.A. Sarhan, M. Sayuti, M. Hamdi, A comprehensive review on machining of titanium alloys, Arab. J. Sci. Eng. 46 (2021) 7087-7123. https://doi.org/10.1007/s13369-021-05420-1
[14] M. Soori, B. Arezoo, A review in machining-induced residual stress, J. New Technol. Mater. 12 (2022) 64-83. https://hal.science/hal-03679993/
[15] B.O. Omiyale, T.O. Olugbade, T.E. Abioye, P.K. Farayibi, Wire arc additive manufacturing of aluminium alloys for aerospace and automotive applications: A review, Mater. Sci. Technol. 38 (2022) 391-408. https://doi.org/10.1080/02670836.2022.2045549
[16] J.P. Chen, L. Gu, G.J. He, A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites, Adv. Manuf. 8 (2020) 294-306. https://doi.org/10.1007/s40436-020-00313-2
