International Journal of Automation, Control and Intelligent Systems
Articles Information
International Journal of Automation, Control and Intelligent Systems, Vol.4, No.2, Jun. 2018, Pub. Date: Jun. 14, 2018
A Fuzzy Approach for Selecting Optimal End Milling Parameters
Pages: 12-18 Views: 392 Downloads: 228
[01] Paul Sawadogo, Mechnical Engineering, National Chung Hsing University, Taichung, Taiwan.
[02] Pai-Chung Tseng, Mechnical Engineering, National Chung Hsing University, Taichung, Taiwan.
[03] Hsueh-Yu Liao, Mechnical Engineering, National Chung Hsing University, Taichung, Taiwan.
In this paper, a fuzzy modeling technique is used for the selection of end milling parameters for a required surface roughness (Ra) with maximum material removal rate (MRR). Spindle speed (N), feed rate (fr) and depth of cut (d) are the inputs and outputs are MRR and SR. Three different levels of each input parameter were used to curry out the experimental work under dry conditions. Optimal sets of parameters were identified using artificial neural network (ANN) for prediction followed by genetic algorithm (GA) multi-objective for optimization. Fuzzy logic model (FLM) was then used to develop a fuzzy rule base in the form of IF-THEN rules for the selection of cutting parameters. The performance of the developed model was evaluated through a validation test. The results show that the average errors of the FLM are 2.463% and 6.08% for Ra and MRR respectively, which is less than 10%.
Artificial Neural Network, Genetic Algorithm, Multi-objective, Fuzzy Logic Model
[01] Puh, F., Jurkovic, Z., Perinic, M., Brezocnik, M. and Buljan. S. (2016). Optimization of machining parameters for turning operation with multiple quality characteristics using grey relational analysis. Tehnički vjesnik 23 (2): 377-382.
[02] Yan, J. and Lin, L. (2013). Multi-objective optimization of milling parameters - the trade-offs between energy, production rate and cutting quality. Journal of cleaner production 52: 462-471.
[03] Moshat, S., Datta, S., Bandyopadhyay, A., and Pal. P. K. (2010). Optimization of CNC end milling process parameters using PCA-based Taguchi method. International Journal of Engineering, Science and Technology 2 (1): 92-102.
[04] Benardos, P. G. and Vosniakos, G. C. (2002). Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robotics and Computer Integrated Manufacturing 18: 343–354.
[05] Zhang, J. Z., Chen, J. C. and Kirby, E. D. (2007). Surface roughness optimization in an end-milling operation using the Taguchi design method. Journal of Materials Processing Technology 184: 233–239.
[06] Hernandez, A. E., Beno, B. T., Repo, J. and Wretland, A. (2016). Integrated optimization model for cutting data selection based on maximal MRR and tool utilization in continuous machining operations. CIRP Journal of Manufacturing Science and Technology 13: 46-50.
[07] Čuš, F. and Župerl. U. (2013). Control strategy for assuring constant surface finish by controlling cutting forces. Transactions of famena XXXVII-3: 41-52.
[08] Kant, G. and Sangwan, K. S. (2015). Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm. Procedia CIRP 31: 453-458.
[09] Ahilan, C. S., Kumanan, N., Sivakumaran, and Dhas, J. E. R. (2013). Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Applied soft computing 13: 1543-1551.
[10] Subramanian, N. and Veerapriya, C. (2015). Optimization of process parameters in milling process for AISI 4340 steel by Taguchi method. International journal of research in Engineering Science and technologies 1 (1): 13-19.
[11] Bhavsar, S. N., Aravindan, S., and Rao, P. V. (2015). Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide. Precision Engineering 40: 131-138.
[12] Patil, V. D., Kamble, S. P., and Bagde, S. T. (2015). Analysis on multi objective optimization of CNC end milling machining parameters of (Al 2024-T4). Journal of Emerging Technologies and Innovative Research 2 (3): 728-732.
[13] Prasad, D. V. V. Krishna and Bharathi, K. (2013). Multi-objective optimization of milling parameters for machining cast iron on machining centre. Research Journal of Engineering Sciences 2 (5): 35-39.
[14] Zhang, G., Patuwo, B. E., and Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14: 35–62.
[15] Zain, A. M., Haron, H., and Sharif, S. (2010). Prediction of surface roughness in the end milling machining using artificial neural network. Expert Systems with Applications 37: 1755–1768.
[16] Shabgard, M. R., Badamchizadeh, M. A., Ranjbary, G., and Amini, K. (2013). Fuzzy approach to select machining parameters in electrical discharge machining (EDM) and ultrasonic-assisted EDN processes. Journal of Manufacturing Systems 32: 32-39.
[17] Yen, J. and Langari, R. (1999). Fuzzy logic: Intelligence, Control, and Information, Prentice Hall, New Jersey.
[18] Tseng, T. L. (BILL), Konada, U., and Kwon, Y. (James). (2016). A novel approach to predict surface roughness in machining operations using fuzzy set theory. Journal of Computational Design and Engineering 3: 1-13.
[19] Patyra, J. M., and. Mlynek, D. M. (1996). Fuzzy logic: Implementation and application. Wiley Teubner.
[20] Kozlowska, E. (2012). Basic principle of fuzzy logic. Vydánodne: 15543 přečtení.
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