Efficient optimization of process parameters in 2.5 D end milling using neural network and genetic algorithm

被引：3
作者：
Kumar D. [1 ]
Chandna P. [2 ]
Pal M. [3 ]
机构：
[1] Department of Mechanical Engineering, JCDM College of Engineering, Sirsa
[2] Department of Mechanical Engineering, National Institute of Technology, Kurukshetra
[3] Department of Civil Engineering, National Institute of Technology, Kurukshetra
来源：
International Journal of System Assurance Engineering and Management | 2018年 / 9卷 / 05期
关键词：
Artificial neural network; Genetic algorithm; Milling; Optimization;
D O I：
10.1007/s13198-018-0737-6
中图分类号：
学科分类号：
摘要：
The study aims at design and development of an integrated system to model and optimize the cutting parameters for identifying and controlling the parameters so as to achieve high level performance and quality in the 2.5 D end milling process. Taguchi’s method is used for experimental design to find out the critical parameters in the 2.5 D end milling process. An optimized artificial neural network (ANN) based on feed-forward back propagation was used to establish the model between 2.5 D end milling parameters and responses. Genetic algorithm (GA) was utilized to find the best combination of cutting parameters providing lower temperature rise in the work piece (Al 6061 T6). As fitness function of GA the output of ANN is used in the study. Cutting parameters include speed, feed, depth of cut and step over. Parameters found out by GA were able to lowers the minimum temperature rise from 19.7 to 17.2 °C with almost 13% decrease. Both the modeling and optimization process was found satisfactory. Also the GA has emerged as an effective tool to optimize 2.5 D end milling process parameters. © 2018, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.
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页码：1198 / 1205
页数：7
相关论文
共 35 条
[1]  
Abukhshim N.A., Mativenga P.T., Sheikh M.A., Heat generation and temperature prediction in metal cutting: a review and implications for high speed machining, Int J Mach Tools Manuf, 46, pp. 782-800, (2006)
[2]  
Arrazola P.J., Ozel T., Umbrello D., Et al., Recent advances in modelling of metal machining processes, CIRP Ann Manuf Technol, 62, 2, pp. 695-718, (2013)
[3]  
Barrow G., A review of experimental and theoretical techniques for assessing cutting temperatures, Ann CIRP, 22, 2, pp. 203-211, (1973)
[4]  
Brinksmeier E., Minke E., Nowag L., Residual stresses in precision components, Proceedings of the 5Th International Conference on Industrial Tooling, pp. 1-21, (2003)
[5]  
Canakci A., Ozsahin S., Varol T., Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks, Powder Technol, 228, pp. 26-35, (2012)
[6]  
Canakci A., Erdemir F., Varol T., Et al., Determining the effect of process parameters on article size in mechanical milling using the Taguchi method: measurement and analysis, Measurement, 46, 9, pp. 3532-3540, (2013)
[7]  
Christos S., Dimitrios S., Neural Networks
[8]  
Feng S.C., Hattori M., Cost and process information modelling for dry machining, Proceedings of the International Workshop for Environment Conscious manufacturing—ICEM-2000, (2000)
[9]  
Ghorbanian J., Ahmadi M., Soltani R., Design predictive tool and optimization of journal bearing using neural network model and multi-objective genetic algorithm, Sci Iran B, 18, 5, pp. 1095-1105, (2011)
[10]  
Goldberg D.E., Genetic algorithms in search, optimization and machine learning, (1989)
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