Estimation of optimal machining control parameters using artificial bee colony

Journal of Intelligent Manufacturing

Volumn 25, Issue 6, 2014, Pages 1463-1472

  Yusup, Norfadzlan ^a   Sarkheyli, Arezoo ^b   Zain, Azlan Mohd ^b   Hashim, Siti Zaiton Mohd ^b   Ithnin, Norafida ^b  

^a UNIVERSITI MALAYSIA SARAWAK   (Malaysia)

^b UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

Author keywords

Abrasive waterjet; Machining; Optimization

Indexed keywords

GENETIC ALGORITHMS; JETS; MACHINING; OPTIMIZATION; QUALITY CONTROL; SIMULATED ANNEALING; SOFT COMPUTING; SURFACE ROUGHNESS;

ABRASIVE WATER-JET; ARTIFICIAL BEE COLONIES; ARTIFICIAL BEE COLONY ALGORITHMS (ABC); CONTROL PARAMETERS; MANUFACTURING INDUSTRIES; MINIMUM SURFACE ROUGHNESS; SOFTCOMPUTING TECHNIQUES; STAND-OFF DISTANCE (SOD);

PARAMETER ESTIMATION;

EID: 84912017087     PISSN: 09565515     EISSN: 15728145     Source Type: Journal    
DOI: 10.1007/s10845-013-0753-y     Document Type: Article

References (31)

1. Azmir, M. A., & Ahsan, A. K. (2008). Investigation on glass/epoxy composite surfaces machined by abrasive water jet machining. Journal of Materials Processing Technology, 198(1–3), 122–128.
2. Caydas, U., & Hascalik, A. (2008). A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. Journal of Materials Processing Technology, 202, 574–582.
3. Chen, H., Lin, J., Yang, Y., & Tsai, C. (2010). Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach. Expert Systems with Applications, 37(10), 7147–7153. doi:10.1016/j.eswa.2010.04.
4. Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214, 108–132.
5. Kolahan, F., & Hamid, K. (2009). Modeling and Optimization of Abrasive Waterjet Parameters using Regression Analysis. World Academy of Science, Engineering and Technology, 59(2009), 488–493.
6. Li, X., Yalaoui, F., Amodeo, L., & Chehade, H. (2012). Metaheuristics and exact methods to solve a multiobjective parallel machines scheduling problem. Journal of Intelligent Manufacturing, 23(4), 1179.
7. Maji, K. & Pratihar, D. K. (2010). Modeling of electrical discharge machining process using conventional regression analysis and genetic algorithms. Journal of Materials Engineering and Performance, 20(7), 1–7.
8. Mokhtar, A., & Xu, X. (2011). Machining precedence of 21/2D interacting features in a feature-based data model. Journal of Intelligent Manufacturing, 22(2), 145–161.
9. Nagendra Parashar, B. S., & Mittal, R. K. (2007). Elements of Manufacturing Processes. New Delhi: Prentice-Hall of India Private Limited.
10. Oduguwa, V., Tiwari, A., & Roy, R. (2005). Evolutionary computing in manufacturing industry: An overview of recent applications. Applied Soft Computing, 5(3), 281–299.
11. Pal, S., Heyns, P. S., Freyer, B. H., Theron, N. J., & Pal, S. K. (2011). Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. Journal of Intelligent Manufacturing, 22(4), 491–504.
12. Pasam, V. K., Battula, S. B., Valli, P. M., & Swapna, M. (2010). Optimizing surface finish in WEDM using the taguchi parameter design method. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 32(2), 107–113.
13. Rao, R. V., & Pawar, P. J. (2009). Modelling and optimization of process parameters of wire electrical discharge machining. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 223(11), 1431–1440.
14. Rao, R. V., Pawar, P. J., & Davim, J. P. (2010). Parameter optimization of ultrasonic machining process using nontraditional optimization algorithms. Materials and Manufacturing Processes, 25(10), 1120–1130.
15. Ridwan, F., Xu, X., & Liu, G. (2012). A framework for machining optimisation based on STEP-NC. Journal of Intelligent Manufacturing, 23(3), 423–441.
16. Roy, R., & Mehnen, J. (2008). Dynamic multi-objective optimisation for machining gradient materials. CIRP Annals Manufacturing Technology, 57(1), 429–432.
17. Saha, P., Singha, A., Pal, S. K., & Saha, P. (2008). Soft computing models based prediction of cutting speed and surface roughness in wire electro-discharge machining of tungsten carbide cobalt composite. International Journal of Advanced Manufacturing Technology, 39(1–2), 74–84.
18. Salehi, M., & Bahreininejad, A. (2011). Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining. Journal of Intelligent Manufacturing, 22(4), 643–652.
19. Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24(6), 946–957.
20. Selvan, M. C. P., & Raju, N. M. S. (2011). Selection of process parameters in abrasive waterjet cutting of copper. International Journal of Advanced Engineering Sciences and Technologies, 7(2), 254–257.
21. Senthilkumar, C., Ganesan, G., & Karthikeyan, R. (2010). Bi-performance optimization of electrochemical machining characteristics of Al/20%SiCp composites using NSGA-II. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 224(9), 1399–1407.
22. Singh, D. K. (2008). Fundamentals of manufacturing engineering. Boca Raton: CRS Press, Taylor and Francis Group.
23. Somashekhar, K. P., Ramachandran, N., & Mathew, J. (2009). Modeling and optimization of process parameters in micro wire EDM by genetic algorithm Retrieved from www.scopus.com.
24. Topal, E. S., & Çoğun, C. (2011). Computer-based estimation and compensation of diametral errors in CNC turning of cantilever bars. Journal of Intelligent Manufacturing, 22(6), 853–865.
25. Yusup, N., & Zain, A. M. (2012). Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007–2011). Expert Systems with Applications, 39(10), 9909–9927.
26. Zain, A. M., Haron, H., & Sharif, S. (2010a). Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert System with Applications, 37, 4650–4659.
27. Zain, A. M., Haron, H., & Sharif, S. (2010b). Prediction of surface roughness in the end milling machining using artificial neural network. Expert System with Applications, 37, 1755–1768.
28. Zain, A. M., Haron, H., & Sharif, S. (2010c). 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.
29. Zain, A. M., Haron, H., & Sharif, S. (2011). Genetic Algorithm and Simulated Annealing to estimate optimal process parameters of the abrasive waterjet machining. Engineering with Computers, 27, 251–259.
30. Zain, A. M., Haron, H., & Sharif, S. (2012a). Integrated ANN-GA for estimating the minimum value for machining performance. International Journal of Production Research, 50(1), 191–213.
31. Zain, A. M., Haron, H., Qasem, S. N., & Sharif, S. (2012b). Regression and ANN models for estimating minimum value of machining performance. Applied Mathematical Modelling, 36(4), 1477–1492.