Multi Objective Optimisation for High Speed End Milling Using Simulated Annealing Algorithm

Article Preview

Abstract:

Machining at high cutting speeds produces higher temperatures in the cutting zone. These temperatures affect the surface quality and flank wear progress. Therefore, determining the optimum cutting levels to achieve the minimum surface roughness and flank wear is an important for it is economical and mechanical issues. This paper presents the optimization of machining parameters in end milling processes by using the simulated annealing algorithm (SAA) as one of the unconventional methods in optimization. The minimum arithmetic mean roughness (Ra) and minimum flank wear length were the objectives. The mathematical models have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response surface Methodology (RSM). This paper presents the optimum cutting parameters: cutting speed, feed rate and depth of cut to achieve the minimum values of surface roughness and minimum flank wear length. The results show that the cutting speed in the range of 200 m/min, feed rate of 0.05 mm/tooth and depth of cut of 0.1mm gave the minimum arithmetic mean roughness (Ra) for 164 and minimum flank wear for 0.0379 in the boundary design of the experiment after 8000 iteration.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

113-116

Citation:

Online since:

July 2015

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] V.N., Gaitonde, S. R., Karnik L. Figueira, & J. P., Davim, Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling. The International Journal of Advanced Manufacturing Technology, 52(1-4), (2010).

DOI: 10.1007/s00170-010-2714-3

Google Scholar

[2] U. Natarajan, P. R. Periyanan & , S. H. Yang, (2011). Multiple-response optimization for micro-endmilling process using response surface methodology. The International Journal of Advanced Manufacturing Technology, 56(1-4), 177-185.

DOI: 10.1007/s00170-011-3156-2

Google Scholar

[3] M., Manohar, J., Joseph, T., Selvaraj, & D. Sivakumar, (2013).

Google Scholar

[4] A., Abdur-Rasheed, & M. Konneh, Optimization of Precision Grinding Parameters of Silicon for Surface Roughness Based on Taguchi Method. Advanced Materials Research, (2011). 264, 997-1002.

DOI: 10.4028/www.scientific.net/amr.264-265.997

Google Scholar

[5] S., Alam, A. K. M., Nurul Amin, A. U., Patwari, & M. Konneh, Prediction and investigation of surface response in high speed end milling of ti-6Al-4V and optimization by genetic algorithm. Advanced Materials Research, 83, (2010). 1009-1015.

DOI: 10.4028/www.scientific.net/amr.83-86.1009

Google Scholar

[6] M. H. F. Al Hazza, E. Y. T. Adesta, M. Riza, & M Y Suprianto, Surface Roughness Optimization in End Milling Using the Multi Objective Genetic Algorithm Approach. Advanced Materials Research, 576, (2012). 103-106.

DOI: 10.4028/www.scientific.net/amr.576.103

Google Scholar

[7] A., Sharma, M. A., Gopichand, M., Pavan, & V. A. Pradesh, Optimization of cutting parameters for surface roughness prediction using artificial neural network in CNC turning. Optimization, (2012). 2(2).

Google Scholar

[8] F., Cus, U., Zuperl, & V. Gecevska, (2007). High speed end-milling optimisation using Particle Swarm Intelligence. Journal of Achievements in Materials and Manufacturing Engineering, 22(2), 75-78.

Google Scholar

[9] N., Baskar, P., Asokan, G., Prabhaharan, & R. Saravanan, (2005). Optimization of machining parameters for milling operations using non-conventional methods. The International Journal of Advanced Manufacturing Technology, 25(11-12), 1078-1088.

DOI: 10.1007/s00170-003-1939-9

Google Scholar

[10] A. M., Zain, H., Haron, & S. Sharif, (2010). Simulated annealing to estimate the optimal cutting conditions for minimizing surface roughness in end milling Ti-6Al-4V. Machining Science and Technology, 14(1), 43-62.

DOI: 10.1080/10910340903586558

Google Scholar

[11] A. M., Zain, H., Haron, & S. Sharif, (2011).

Google Scholar

[12] S, Kirkpatrick, C, Gelatt, M. Vecchi (1983) Optimization by simulated annealing. Science 220: 671–680.

DOI: 10.1126/science.220.4598.671

Google Scholar