Optimization of Machining Parameter for Surface Roughness in Turning GFRP Composite Using RSM-GA Approach

Md. Rezaul Karim¹, Sayem Ahmed¹, Samia Salahuddin¹ and Md. Rezaul Karim^1,

¹Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

Cite this paper:
Md. Rezaul Karim, Sayem Ahmed, Samia Salahuddin and Md. Rezaul Karim. Optimization of Machining Parameter for Surface Roughness in Turning GFRP Composite Using RSM-GA Approach. American Journal of Materials Science and Engineering. 2018; 6(1):18-25. doi: 10.12691/ajmse-6-1-4

Abstract

This paper deals with the analysis of surface roughness in turning GFRP composite through experimental investigation and Response surface methodology (RSM) based optimization modeling incorporated with Genetic Algorithm (GA). The investigation has been carried out in dry condition where cutting speed, feed rate and depth of cut has been considered as input parameters to check the desired surface roughness response. This experiment has been designed using RSM central composite design (CCD). Afterwards the response model has been formulated using quadratic RSM model and Genetic algorithm. The correlation coefficient value of 0.9989 suggests the adequacy of the formulated model. Main effect plot and 3D surface plot have been used to evaluate the effect of input parameters followed by Desirability Function Analysis (DFA) through response surface equation of the machining response. Machining parameters were then optimized using GA approach which indicated that to attain advantageous machining response cutting speed and feed rate need to be at 78 m/min and 0.10 mm/rev respectively. These findings are also analogous with the result of DFA which validates both the model. By employing the model, surface roughness of as minimum as 0.056 µm can be achieved.

Keywords:
GFRP composite turning surface roughness response surface methodology desirability function analysis genetic algorithm

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References:

[1]	Hussain et al. Machinability of glass fiber reinforced plastic composite material, International journal of Engineering, Science and Technology, 2011, Volume 3, pp. 103-118.
[2]	Jarukumjorn, K.; Suppakarn, N. Effect of glass fiber hybridization on properties of sisal fiber polypropylene composites, Composites: Part B, 2009; Volume 40, pp. 623-627, ISSN: 1359-8368.
[3]	Dwivedi U.K.; Chand N., Influence of MA-G-PP on abrasive wear behavior pf chopped fiber reinforced polyplropylene composites, Journal of Material Process Technology, 2009; Volume 209, pp. 5371-5375, ISSN: 0924-0136.
[4]	Azmi A. I., Lin R. J. T. & Bhattacharyya D., Machinability study of glass fibre-reinforced polymer composites during end milling, The International Journal of Advanced Manufacturing Technology; 2013 Volume 64, Issue 1-4, pp 247-261.
[5]	Fetecau C., Study of cutting force and surface roughness in the turning of polytetrafluoroethylene composites with a polycrystalline diamond tool, Measurement 2012; Volume 45, pp. 1367-1379.
[6]	Ranjan B., Ranjan Kr. G., Kalita K. and Banerjee S., Simultaneous prediction of delamination and surface roughness in drilling GFRP composite using ANN, International Journal of Plastics Technology, 2016; Volume 20, Issue 2, pp 424-450.
[7]	Reddy S., Optimization of Surface Roughness and Delamination Damage of GFRP Composite Material in End Milling using Taguchi Design Method and Artificial Neural Network, Procedia Engineering, 2013; Volume 64, pp 785-794.
[8]	Erkan O., Birhan I. Çiçek A. and Kara F. Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network, Application of Composite Materials, 2013; Volume 20, pp 517-536.
[9]	Palanikumar K.; Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology, Materials and Design, 2007; Volume 28. pp 2611-2618.
[10]	Varga Cs., Miskolczi N., Bartha L., and Lipóczi G., Improving the mechanical properties of glass-fiber reinforced polyester composites by modification of fiber surface, Materials & Design, 2010; Volume 31, Issue 1, pp 185-193.
[11]	Wang J. A machinability study of polymer matrix composites using abrasive waterjet cutting technology, Journal of Materials Processing Technology, 1999; Volume 94, Issue 1, pp 30-35.
[12]	Mata F., Petropoulos G. and Ntziantzias I., Davim J., A surface roughness analysis in turning of polyamide PA-6 using statistical techniques; Int. J. Materials and Product Technology, 2010; Volume 37, pp 173-187.
[13]	Palanikumar K., Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling, International Journal of Advanced Manufacturing Technology, 2008; Volume 36,pp 19-27.
[14]	Palanikumar K., Karunamoorthy L., and R. Karthikeyan,, Assessment of factors influencing surface roughness on the machining of glass fiber-reinforced polymer composites, Materials and Design, 2010; Volume 27, pp 862-871.
[15]	Khuri A.I., and Mukhopadhyay S., Response surface methodology, Wiley interdisciplinary reviews: Computational sciences, Volume 2, Issue 2, pp. 128-149.
[16]	Pettersson F., Biswas A., Sen P.K., Saxena H., and Chakraborti N. Analyzing leaching data for low-grade manganese ore using neural nets and multi objective genetic algorithms, Materials and Manufacturing Processes, 2009; Volume 24, Issue 3, pp 320-330.
[17]	Nakhjavani O. B. and Ghoreishi M., Multi Criteria Optimization of Laser Percussion Drilling Process Using Artificial Neural Network Model Combined with Genetic Algorithm, Materials and Manufacturing Processes, 2006; Volume 21, Issue 1, pp 11-18.
[18]	Pal Sukhomay, Pal Surjya K., Samantaray Arun K., Determination of optimal pulse metal inert gas welding parameters with a Neuro-GA Technique, Materials and Manufacturing Processes, 2010; Volume 25, Issue 7, pp. 606-615.
[19]	Somashekhar K. P., Ramachandran N., Mathew Jose., Optimization of material removal rate in micro-EDM using Artificial Neural Network and Genetic Algorithms, Materials and Manufacturing Processes, 2010; Volume 25, Issue 6, pp. 467-47.
[20]	Myers, R.H., Montgomery, D. C., & Anderson Cook, C.M, Response surface methodology: process and product optimization using design experiments: John wiley & sons, 2016.
[21]	M.D’ Addona D. Teti R. Genetic Algorithm based optimization of cutting parameters in turning processes, Procedia CIRP, 2013; Volume 7, pp. 323-328.