Analysis of Al7136 surface roughness in end milling process based on discriminant analysis
Alina Bianca POP, Aurel Mihail TITU
Abstract. The objective of this research is to determine how cutting parameters influence the transversal surface roughness of Al7136 aluminum alloy when subjected to end milling. To achieve this, 150 experiments were performed, systematically varying the cutting speed (v), depth of cut (ap), and feed per tooth (fz). The method used for analysis is discriminant analysis, which generated three discriminant functions. The results indicate a significant correlation between these variables and surface roughness. The discriminant functions provided an accurate classification of observations into different levels of roughness, and the coefficients of these functions showed the relative importance of each independent variable in discriminating between different levels of roughness. Covariance and correlation analyses were performed to further understand the interactions between the independent variables within each experimental group. The conclusions suggest that adjusting cutting parameters can be performed to achieve a reduction in the roughness of the machined surface, contributing to the improvement of product quality in end milling of the Al7136 alloy.
Keywords
End Milling, Al7136 Aluminum Alloy, Surface Roughness, Discriminant Analysis, Cutting Parameters
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: Alina Bianca POP, Aurel Mihail TITU, Analysis of Al7136 surface roughness in end milling process based on discriminant analysis, Materials Research Proceedings, Vol. 46, pp 135-142, 2024
DOI: https://doi.org/10.21741/9781644903377-18
The article was published as article 18 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] N.S. Bajaj, A.D. Patange, R. Jegadeeshwaran, K.A. Kulkarni, R.S. Ghatpande, A.M. Kapadnis, A Bayesian optimized discriminant analysis model for condition monitoring of face milling cutter using vibration datasets, J. Nondestruct. Eval. Diagn. Progn. Eng. Syst. 5 (2022) 021002. https://doi.org/10.1115/1.4051696
[2] 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
[3] I.P. Okokpujie, O.O. Ajayi, S.A. Afolalu, A.A. Abioye, E.Y. Salawu, M. Udo, O.M. Ikumapayi, 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. (IJMET) 9 (2018) 587-600. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=1 [4] N.E. Karkalos, P. Karmiris-Obratański, S. Kurpiel, K. Zagórski, A.P. Markopoulos, Investigation on the surface quality obtained during trochoidal milling of 6082 aluminum alloy, Machines 9 (2021) 75. https://doi.org/10.3390/machines9040075
[5] 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
[6] 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
[7] 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
[8] C.A. Zhou, K. Guo, J. Sun, B. Yang, J. Liu, G. Song, Z. Jiang, Tool condition monitoring in milling using a force singularity analysis approach, Int. J. Adv. Manuf. Technol. 107 (2020) 1785-1792. https://doi.org/10.1007/s00170-019-04664-4
[9] S. Das, G. Kibria, B. Doloi, B. Bhattacharyya (Eds.), Advances in Abrasive Based Machining and Finishing Processes, Springer Nature, 2020. https://link.springer.com/book/10.1007/978-3-030-43312-3
[10] X.A. Vasanth, P.S. Paul, A.S. Varadarajan, A neural network model to predict surface roughness during turning of hardened SS410 steel, Int. J. Syst. Assur. Eng. Manag. 11 (2020) 704-715. https://doi.org/10.1007/s13198-020-00986-9
[11] Y.C. Lin, K.D. Wu, W.C. Shih, P.K. Hsu, J.P. Hung, Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network, Appl. Sci. 10 (2020) 3941. https://doi.org/10.3390/app10113941
[12] P. Saini, P.K. Singh, Optimization of end milling parameters for rough and finish machining of Al-4032/3% SiC metal matrix composite, Eng. Res. Express 3 (2021) 045009. https://doi.org/10.1088/2631-8695/ac2e11
[13] S.H. Musavi, M. Sepehrikia, B. Davoodi, S.A. Niknam, 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. (2022) 1-20. https://doi.org/10.1007/s00170-021-08349-9
[14] R.A. Mali, R. Aiswaresh, T.V.K. Gupta, The influence of tool-path strategies and cutting parameters on cutting forces, tool wear and surface quality in finish milling of Aluminium 7075 curved surface, Int. J. Adv. Manuf. Technol. 108 (2020) 589-601. https://doi.org/10.1007/s00170-020-05414-7
[15] M. Zhou, Y. Chen, G. Zhang, Force prediction and cutting-parameter optimization in micro-milling Al7075-T6 based on response surface method, Micromachines 11 (2020) 766. https://doi.org/10.3390/mi11080766
