Estimation of Roughness of Machined Surface Using Artificial Neural Networks
Authors
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Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia ,IN
Firdous Ali Khan -
Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia ,INPritam Chatterjee
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Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia ,INSumit Mandi
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Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia ,INUpendra Kumar Shaw
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Department of Mechanical Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia ,INSantanu Das
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Department of Computer Science & Engineering, Kalyani Govt. Engineering College, Kalyani- 741235, Nadia ,INSupriyo Banerjee
Keywords:
Machining, shaping, surface roughness, estimation, ANN, Neural Networks.Abstract
Setting appropriate machining parameters can give desired finish of a job. Selectionof such parameters needs time consuming and costly experimentation. In this work,
artificial neural networks (ANN) is used to predict roughness parameters of machined
surface to reduce time and cost involved for the experiments. Surface roughness
parameters assessed through ANN are compared with the observed data and an
accuracy of 95.5% is reported.
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A Khorasani, and MRS Yazdi,Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation, The International Journal of Advanced Manufacturing Technology, Vol. 93(1-4), page 141151, 2017.
A Parida, B Routara, and R Bhuyan, Surface roughness model and parametric optimization in machining of GFRP composite: Taguchi and Response surface methodology approach, Materials Today: Proceedings, Vol. 2(4-5), page 3065-3074, 2015.
Y H Tsai, J C Chen, and S J Lou, An in-process surface recognition system based on neural networks in end milling cutting operations, International Journal of Machine Tools and Manufacture, Vol. 39(4), page 583–605, 1999.
P G Benardos, and G C Vosniakos, Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments, Robotics and ComputerIntegrated Manufacturing, Vol. 18(5-6), page 343–354, 2002.
U Zuperl, E Kiker, and F Cus, Optimization in ballend milling by using adaptive neural controller, in Proceedings of the IEEE International Conference on Industrial Technology (ICIT ’03), page 393–398, December 2003.
S Das, R Roy, and AB Chattopadhyay, Evaluation of wear of turning carbide inserts using neural networks, International Journal of Machine Tools and Manufacture, Vol. 36(7), page 789-797, 1996.
S Das, PP Bandyopadhyay, and AB Chattopadhyay, Neural-networks-based tool wear monitoring in turning medium carbon steel using a coated carbide tool, Journal of Materials Processing Technology, Vol. 63(1-3), page 187-192, 1997.
S Das, On wear monitoring of TiN coated tools- part II: with neural networks, Proceedings of the 14th International Conference on Robotics and Factories of the Future, Coimbatore, India, page 645-653, 1998.
X Li, X Guan, and Y Li, A hybrid radial basis function neural network for dimensional error prediction in end milling, in Proceedings of the International Symposium on Neural Networks, Vol.
of Lecture Notes in Computer Science, page 743–748, Dalian, China, August 2004.
B Ozcelik, H Oktem, and H. Kurtaran, Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm, International Journal of Advanced Manufacturing Technology, Vol. 27(3-4), page 234– 241, 2005.
H Oktem, An integrated study of surface roughness for ¨ modelling and optimization of cutting parameters during end milling operation, International Journal of Advanced Manufacturing Technology, Vol. 43(9-10), page 852–861, 2009.
I N Tansel, B Ozcelik, W Y Bao, et al., Selection of optimal cutting conditions by using GONNS, International Journal of Machine Tools & Manufacture, Vol. 46, No 1, page 26–35, 2006.
T Erzurumlu, and H Oktem, Comparison of response surface model with neural network in determining the surface quality of moulded parts, Materials and Design, Vol. 28, no. 2, page 459–465, 2007.
B P Huang, J C Chen, and Y Li, Artificial-neuralnetworksbased surface roughness Pokayoke system for end-milling operations, Neurocomputing, Vol. 71(4–6), page 544–549, 2008.
M I Hossain, A K M Amin, and A U Patwari, Development of an artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy, in Proceedings of the International Conference on Computerand Communication Engineering (ICCCE ’08), page 1321–1324, May 2008.
I A El-Sonbaty, U A Khashaba, A I Selmy, and A I Ali, Prediction of surface roughness profiles for milled surfacesusing an artificial neural network and fractal geometry approach, Journal of Materials Processing Technology, Vol. 200(1–3), page 271– 278, 2008.
A M Zain, H Haron, and S Sharif, Application of regression and ANN techniques for modeling of the surface roughness in end milling machining process, in Proceedings of the 3rd Asia International Conference on Modelling and Simulation (AMS’09), page 188–193, May 2009.
A M. Zain, H. Haron, and S Sharif, Artificial neural network for predicting machining performance of uncoated carbide(WC-Co) in milling machining operation, in Proceedings of the International Conference on Computer Technology and Development (ICCTD ’09), page 76–80, November 2009.
A M Zain, H Haron, and S Sharif, Prediction of surface roughness in the end milling machining using artificial neural network, Expert Systems with Applications, vol. 37(2), page 1755–1768, 2010.
P Munoz-Escalona and P G Maropoulos, Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351, Journal of Materials Engineering and Performance, vol. 19(2), page 185–193, 2010.
A Mukherjee and S Das, A simple online tool condition monitoring system using artificial neural Networks, IOP Conf. Series: Materials Science and Engineering, Vol.1080, No 012021, 2021.
G Zhang, B E Patuwo and M. Y Hu, Forecasting with artificial neural networks: The state of the art, International Journal of Forecasting, Vol.14, page 35–62, 1998.
G Quintana, M L Garcia-Romeu, and J Ciurana, Surface roughness monitoring application based on artificial neural networks for ball-end milling operations, Journal of Intelligent Manufacturing, Vol.22, page 607–617, 2011.
