Artificial Neural Networks based prediction and Multi Response Optimization on EDM of Aluminium/Fly ash composites

AUTHOR(S):

TITLE

Artificial Neural Networks based prediction and Multi Response Optimization on EDM of Aluminium/Fly ash composites

ABSTRACT

Aluminium Metal matrix composites reinforced with fly ash particles of three different particle size ranges ((53–75) μm, (75–103) μm and (103–125) μm) were fabricated using stir casting technique. Electrical discharge machining (EDM) was employed to machine the composite materials with copper electrode. The influence of EDM process parameters namely peak current, pulse-on-time, pulse-off-time, particle size and the percentage fly ash on Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness(SR) were investigated. Artificial Neural Network (ANN) model was employed to predict the material removal rate, tool wear rate and surface roughness of the composites. The experimental values coincide with the predicted values of the proposed networks. The process parameters are then optimized using desirability based multi response optimization technique to maximize the MRR and minimize both TWR and SR. Increase in peak current and pulse-on time increased the MRR while increase in pulse-off time, percentage fly ash and fly ash particle size decreased the MRR.

KEYWORDS

Composites, EDM, ANN, Multi response optimization

REFERENCES

[1]. B.Mohan,A. Rajadurai, K.G.Satyanarayana, Electric discharge machining of Al–SiC metal matrix composites using rotary tube electrode. Journal of Materials Processing Technology, Vol.153, 2004, pp. 978–985.

[2]. Norliana Mohd Abbas, Darius G Solomon, Md Fuad Bahari, A review on current research trends in electrical discharge machining (EDM), International Journal of Machine Tools & Manufacture, Vol.47, 2007, pp. 1214 -1228.

[3]. K.H.Ho, S.T. Newman, State of the art electrical discharge machining (EDM), International Journal of Machine Tools & Manufacture, Vol.43, 2003, pp. 1287–1300.

[4]. H.K.Kansal, Sehijpal Singh, Pradeep Kumar, Technology and research developments in powder mixed electric discharge machining (PMEDM), Journal of Materials Processing Technology, Vol.184, 2007, pp.32–41.

[5]. Muslim Mahardika, Kimiyuki Mitsui, A new method for monitoring micro-electric discharge machining processes. International Journal of Machine Tools & Manufacture, Vol.48, 2008, pp.446-458.

[6]. P.Narender Singh, K.Raghukandan, M.Rathinasabapathi, B.C.Pai, Electric discharge machining of Al–10%SiCP as-cast metal matrix composites. Journal of Materials Processing Technology, Vol.155, 2004, pp. 1653-1657.

[7]. D.Gurgui, E.Vazquez, I.Ferrer, Influence of the Process Parameters to Manufacture Micro-cavities by Electro Discharge Machining (EDM), Procedia Engineering, Vol. 63, 2013, pp. 499 – 505.

[8]. Fabio N Leao, Ian R Pashby, A review on the use of environmentally-friendly dielectric fluids in electrical discharge machining. Journal of Materials Processing Technology, Vol.149, 2004, pp. 341–346.

[9]. Yanzhen Zhang, Yonghong Liu, Yang Shen, Renjie Ji, Zhen Li, Chao Zheng, Investigation on the influence of the dielectrics on the material removal characteristics of EDM, Journal of Materials Processing Technology, Vol. 214, 2014. Pp.1052 - 1061.

[10]. Sushant Dhar, Rajesh Purohit, Nishant Saini, Akhil Sharma, G. Hemath Kumar, Mathematical modeling of electric discharge machining of cast Al–4Cu–6Si alloy–10 wt.% SiCP composites, Journal of Materials Processing Technology, Vol.194, 2007, pp.24–29.

[11]. F.Q.Hu, F.Y. Cao, B.Y. Song, P.J.Hou, Y.Zhang, K.Chen, J.Q.Wei, Surface properties of SiCp/Al composite by powdermixed EDM, Procedia CIRP , Vol.6,2013, pp. 101 – 106.

[12]. K.D.Chattopadhyay, S.Verma, P.S.Satsangi, P.C.Sharma,Development of empirical model for different process parameters during rotary electrical discharge machining of copper– steel (EN-8) system, Journal of Materials Processing Technology, Vol.209, 2009, pp.1454–1465.

[13]. C.Diver, J.Atkinson, H.J. Helml, L.Li, Micro-EDM drilling of tapered holes for industrial applications. Journal of Materials Processing Technology, Vol.149, 2004, pp.296–303.

[14]. Sanjeev Kumar, Rupinder Singh, T.P.Singh, B.L. Sethi, BL, Surface modification by electrical discharge machining: A review, Journal of Materials Processing Technology, Vol.209 2009, pp. 3675–3687.

[15]. L.Li, Y.B.Guo, X.T.Wei, W. Li, Surface integrity characteristics in wire-EDM of inconel 718 at different discharge energy, Procedia CIRP, Vol. 6, 2013, pp.220 – 225.

[16]. K.Ravi Kumar, K.M. Mohanasundaram, G.Arumaikkannu, R.Subramanian, Analysis of Parameters Influencing Wear and Frictional Behavior of Aluminum–Fly Ash Composites, Tribology Transactions, Vol.55, 2012, pp. 723-729.

[17]. Krishnan Ravi Kumar, Kothavady Mylsamy Mohanasundaram, Ganesan Arumaikkannu, Ramanathan Subramanian, Effect of particle size on mechanical properties and tribological behaviour of aluminium/fly ash composites. Science and Engineering of Composite Materials, Vol.19, 2012, pp.247– 253.

[18]. Lada A Gyurova, Klaus Friedrich, Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites, Tribology International, Vol.44, 2011, pp. 603–609.

[19]. Xu LiuJie, Paulo Davim J, Rosaria Cardoso, Prediction on tribological behaviour of composite PEEK-CF30 using artificial neural networks, Journal of Materials Processing Technology,Vol.189, 2007, pp.374–378.

[20]. P.Sathyabalan, V.Selladurai, P. Sakthivel, ANN Based Prediction of Effect of Reinforcements on Abrasive Wear Loss and Hardness in a Hybrid MMC, American Journal of Engineering and Applied Sciences,Vol. 2, No.1, 2009, pp.50-53.

[21]. S.N.Joshi, S.S.Pande, Intelligent process modeling and optimization of die-sinking electric discharge machining, Applied Soft Computing Vol.11, 2011, pp.2743- 2755.

[22]. D.A.Fadare, E.O. Ezugwu, J.Bonney, Modeling of Tool Wear Parameters in High- Pressure Coolant Assisted Turning of Titanium Alloy Ti-6Al-4V Using Artificial Neural Networks, The Pacific Journal of Science and Technology, Vol. 10, No.2, 2009, pp.68-76.

[23]. K. Ravi Kumar, K.M.Mohanasundaram, G.Arumaikkannu, R.Subramanian, Artificial neural networks based prediction of wear and frictional behaviour of aluminium (A380)– fly ash composites, Tribology-Materials, surfaces and interfaces Vol.6, No.1, 2012, pp. 15-19.

[24]. Jiahua Zhu, Yijun Shi, Xin Feng, Huaiyuan Wang, Xiaohua Lu, Prediction on tribological properties of carbon fiber and TiO2 synergistic reinforced polytetrafluoroethylene composites with artificial neural networks, Materials and Design, 30,2009, pp. 1042–1049.

[25]. J.T.Lin, D.Bhattacharyya, V. Kecman, Multiple regression and neural networks analyses in composites machining, Composites Science and Technology, Vol.63,2003, pp. 539-548.

[26]. Chang-Chun Zhou, Guo-Fu Yin, Xiao-Bing Hu, Multi-objective optimization of material selection for sustainable products: Artificial neural networks and genetic algorithm approach, Materials and Design, Vol.30, 2009, pp. 1209–1215.

[27]. Debaprasanna Puhan, Siba Sankar Mahapatra, Jambeswar Sahu, Layatitdev Das , A hybrid approach for multi-response optimization of non-conventional machining on AlSiCp MMC, Measurement, 46, 2013, pp. 3581–3592.

[28]. Emel Kuram, and Babur Ozcelik, Multiobjective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill, Measurement 46,2013, pp. 1849–1864.

[29]. Amirhossein Amiri, Mahdi Bashiri, Hamed Mogouie, Mohammad Hadi Doroudyan (2012) Non-normal multi-response optimization by multivariate process capability index, Scientia Iranica E, Vol. 19,No.6,2012, pp. 1894 - 1905.

[30]. Ibrahim N Tansel, Mustafa Demetgul, Hasan Okuyucu, Ahmet Yapici, Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network, The International Journal of Advanced Manufacturing Technology, 48, 2010,pp.95– 101.

[31]. M. Abdelhay, Application of Artificial Neural Networks to Predict the Carbon Content and the Grain Size for Carbon Steels, Egyptian Journal of Solids. Vol.25, No.2, 2002, pp.229-243.

[32]. Jiahua Zhu, Yijun Shi, Xin Feng, Huaiyuan Wang, Xiaohua Lu, Prediction on tribological properties of carbon fiber and TiO2 synergistic reinforced polytetra fluoroethylene composites with artificial neural networks, Materials and Design, Vol. 30, 2009, pp.1042–1049.

[33]. F.S.Lobato, M.N. Sousa, M.A. Silva, A.R.Machado, Multi-objective optimization and bio-inspired methods applied to machinability of stainless steel, Applied Soft Computing, Vol.22, 2014, pp. 261–271.

[34]. G.Derringer, R.Suich, Simultaneous optimization of several response variables. Journal of Quality Technology, Vol. 12, No.4,1980, pp. 214- 219.

[35]. S.Gopalakannan, T.Senthilvelan, Parametric study of electrical discharge machining process parameters on machining of cast Al/B4C metal matrix nanocomposites, Journal of Engineering Manufacture, Vol.227,No.7, 2013, pp. 993-1004.

[36]. S.Gopalakannan, T.Senthilvelan, Application of response surface method on machining of Al–SiC nano-composites, Measurement, Vol.46, 2013, pp.2705–2715.

[37]. R.Kumar Bansal, A.Kumar Goel, M. Kumar Sharma, MATLAB and its applications in engineering, 1st edn; 2009, NewDelhi, Pearson Education.

Cite this paper

K. Ravi Kumar, V.S.Sreebalaj. (2017) Artificial Neural Networks based prediction and Multi Response Optimization on EDM of Aluminium/Fly ash composites. International Journal of Theoretical and Applied Mechanics, 2, 145-156