Prediction of Surface Roughness of Mild Steel Alloy in CNC Milling Process Using ANN and GA Technique

Abdulridha, Hind H.

doi:10.30684/etj.v38i12A.1579

	Prediction of Surface Roughness of Mild Steel Alloy in CNC Milling Process Using ANN and GA Technique
Engineering and Technology Journal
Article 11, Volume 38, 12A, 2020, Pages 1842-1851 PDF (1.15 M)
Document Type: Research Paper
DOI: 10.30684/etj.v38i12A.1579
Author
Hind H. Abdulridha
Production Engineering and Metallurgy Department, University of Technology-Iraq.
Abstract
In this paper, Analysis Of Variance (ANOVA), Artificial Neural Network (ANN), and Genetic Algorithm (GA) have been studied to predict the effect of milling parameters on the Surface Roughness (Ra) during machining of mild steel alloy. The milling experiments carried out based on the Taguchi design of experiments method using (L16) orthogonal array with 3 factors and 4 levels. The influence of three independent variables such as spindle speed (910, 930, 960, and 1000 rpm), feed rate (93, 95, 98, and 102 mm/min), and Tool Diameter (8, 10, 12, and 14 mm) on the Surface Roughness (Ra) were tested and analyzed with (ANOVA) to predict the response which indicates that spindle speed was the most significant factor effecting on Surface Roughness (Ra). Artificial Neural Network (ANN) and numerical methods are used widely for modeling and predict the performance of manufacturing technologies. Neural Network technique with 2 hidden layers, 10 neurons size, 1000 epochs, and Trainlm transfer function is used to predict the result. The Genetic Algorithm (GA) has been utilized to find optimal cutting conditions during a milling process. From the results, the optimal value of spindle speed is (930 rpm), feed-rate is (95 mm/min) and tool diameter is (8 mm). This network structure is capable of predicting the Surface Roughness (Ra) well to optimize the milling parameters. Artificial Neural Network (ANN) predicted results indicate good agreement between the experimental and the predicted values
Keywords
Artificial Neural Network (ANN); ANOVA; Genetic Algorithm (GA); Surface Roughness (Ra). Taguchi method

References
[1] D.B. prakash , G.R. Balaji , A.G. chand, V.A. kumar, D.V.N.Prabhaker, “Optimization of machining parameters for aluminium alloy 6082 in cnc end milling,” International Journal of Eng. Research and Applications (IJERA), ISSN: 2248-9622, Vol. 3, Issue 1, pp.505-510, 2013. [2] F. Puh, Z. Jurkovic, M. Perinic, M. Brezocnik and S. Buljan, “Optimization of machining parameters for turning operation with multiple quality characteristics using Grey Relational Analysis,” Tehnicki vjesnik, vol.23, no.2, pp.377-382, 2016. [3] M.S. Mahendra and B Sibin, “Optimization of milling parameters for minimum surface roughness using Taguchi method,” IOSR Journal of Mechanical and Civil Eng. (IOSR-JMCE), e-ISSN: 2278-1684, p-ISSN: 2320-334X, pp.01-05, 2016. [4] S. Sakthivelu, M. Meignanamoorthy, M. Ravichandran and M. Kumar, “Effect of machining parameters on surface roughness and material removal rate in CNC end milling,” International Journal of Scientific [5] ,” ICONNECT Research and Eng. Studies (IJSRES), vol.2, Issue 4, ISSN: 2349-8862, 2015. [6] P. Pothys, R. Selvam, and A. S. Kumar, “Evaluation of optimal machining parameters for turning by using genetic algorithm - 2k18, ” Conference Proceedings, International Journal of Eng. Research & Technology (IJERT), ISSN: 2278-0181, Volume 6, Issue 07, 2018. [7] A. Zerti , M.A. Yallese, O. Zerti , M. Nouioua and R. Khettabi, “Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420,” Journal of Mechanical Eng. Science, Vol. 0, No. 0, pp. 1–24, 2019. [8] Z. Amarta, B. O Pramoedyo, S. Sutikno, and R. Norcahyo, “Multi response optimization in vulcanization process using backpropagation neural network-genetic algorithm method for reducing quality loss cost,” AIP Conference Proceedings 2114, 2019. [9] P. A. L. Laot, and S. Sampurno , “Multi-response optimization of cutting force and surface roughness in carbon fiber reinforced polymer end milling using back propagation neural network and genetic algorithm,” AIP Conference Proceedings 2114, 2019. [10] J. Mumtaz, Z. Li, M. Imran, Lei Yue, M. Jahanzaib, S. Sarfraz, E. Shehab, S. Oluwarotimi and K. Afzal, “Multi-objective optimization for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm,” Advances in Mechanical Eng., Vol. 11, No. 4, pp.1–13, 2019. [11] A. Batwara and P. Verma, “Influence of process parameters on surface roughness and material removal rate during turning in CNC lathe – an Artificial Neural Network and surface response methodology,” International Journal of Recent Advances in Mechanical Eng. (IJMECH), vol.5, No.1, 2016. [12] M. J. Madic and M. R. Radovanovic, “Optimal selection of ANN training and architectural parameters using Taguchi method: A case study,” FME Transations, vol.39, No.2, pp.79-86, 2011. [13] S. Cetin and T. Kivak, “Optimization of the machining parameters for the turning of 15-5 PH stainless steels using the Taguchi method,” Materiali in Technologiji, vol.51, No.1, pp.133-140, 2017. [14] S. Karabulut, “Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural netwotks and Taguchi method,” Measurement, vol.66, pp.139-149, 2015. [15] M. M.H. AL-Khafaji, H. L. Alwan and B. M.H. Albaghdadi, “Roughness assessment for machined surfaces in turning operation using neural Network,” Eng. & Tech. Journal, Vol.32, Part (A), No.5, 2014. [16] S. Dave , Jay J. Vora , N. Thakkar , A. Singh, S. Srivastava, B. Gadhvi, V. Patel, A. Kumar, “Optimization of EDM drilling parameters for Aluminum 2024 alloy using Response Surface Methodology and Genetic Algorithm,” Key Eng. Materials, ISSN: 1662-9795, Vol. 706, pp. 3-8, 2016.
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