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Licensed Unlicensed Requires Authentication Published by De Gruyter April 29, 2017

Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes

Moth-Flame-Algorithmus zur Bestimmung optimaler Maschinenparameter in Bearbeitungsprozessen
  • Betül Sultan Yıldız and Ali Rıza Yıldız
From the journal Materials Testing

Abstract

In this research, a newly developed moth-flame optimization algorithm (MFO) is presented for solving optimization problems in manufacturing industry. A well-known milling optimization problem is solved to emphasize the effectiveness of the MFO in the optimization of manufacturing problems. In the optimization problem solved in this paper, the main aim is to maximize the profit rate for multi-tool milling operations considering difficult constraints. The results demonstrate that the MFO is an effective optimization method for the solution of manufacturing optimization problems.

Kurzfassung

In der diesem Beitrag zugrunde liegenden Forschungsarbeit wurde ein neuentwickelter Moth-Flame-Optimierungsalgorithmus (MFO) zur Lösung von Optimierungsaufgaben in der Fertigungsindustrie eingesetzt. Hierzu wurde eine wohlbekannte Optimierungsaufgabe beim Fräsen gelöst, um die Effektivität des MFO in der Optimierung von Bearbeitungsprozessen herauszustellen. Bei dieser Aufgabe bestand das Hauptziel darin, die Profitrate für Multi-Werkzeug-Fräsoperationen unter Berücksichtigung schwieriger Bedingungen zu maximieren. Die Ergebnisse zeigen, dass der MFO ein effektives Optimierungsverfahren für die Lösung von Optimierungsaufgaben in der Fertigung darstellt.


*Correspondence Address, Prof. Dr. Ali Riza Yildiz, Department of Mechanical Engineering, Bursa Technical University, Bursa, Turkey, E-mail: ,

Dr. Betül Sultan Yıldız completed her BSc and MSc degrees at Uludağ University, Bursa, Turkey andreceived her PhD in Mechanical Engineering from Bursa Technical University, Turkey. Her research interests are optimal design, shape optimization, topology optimization, topography optimization, structural optimization methods and meta-heuristic optimization algorithms as well as applications to industrial problems.

Ali Rıza Yıldız is Professor in the Department of Mechanical Engineering, Bursa Technical University (BTU), Bursa, Turkey. In 2015, he was a winner of TÜBA-GEBİP Young Scientist Outstanding Achievement Award given by the Turkish Academy of Sciences (TÜBA). He has served as Associate Editor of Elsevier's Information Sciences Journal and as Associate Editor of Springer's Journal of Intelligent Manufacturing. His research interests are vehicle design, vehicle crashworthiness, shape, topology, topography optimisation of vehicle components, optimization algorithms and sheet metal forming.


References

1 F. W.Taylor: On the Art of Cutting Metals, Trans. American Soc. Mech. Engineers28 (1907), No. 37Search in Google Scholar

2 A. R.Yıldız: Cuckoo search algorithm for the selection of optimal machining parameters in milling operations, International Journal of Advanced Manufacturing Technology64 (2013), pp. 556110.1007/s00170-012-4013-7Search in Google Scholar

3 E. J. A.Armarego, A. J. R.Simith, J.Wang: Computer-aided constrained optimisation analyses strategies for multipass helical tooth milling operation, Annals of the CIRP43 (1994), No. 1, pp. 43744210.1016/S0007-8506(07)62248-3Search in Google Scholar

4 E. J. A.Armarego, A. J. R.Smith, J.Wang: Constrained optimization strategies CAM software for single-pass peripheral milling, International Journal of Production Research31 (1993), No. 9, pp. 2139216010.1080/00207549308956849Search in Google Scholar

5 A. R.Yıldız: Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations, Applied Soft Computing13 (2013), pp. 1433143910.1016/j.asoc.2012.01.012Search in Google Scholar

6 A. R.Yıldız: Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach, Information Sciences220 (2013), pp. 39940710.1016/j.ins.2012.07.012Search in Google Scholar

7 N.Baskar, P.Asokan, R.Saravanan, G.Prabhaharan: Optimization of machining parameters for milling operations using non-conventional methods, International Journal of Advance Manufacturing Technology25 (2005), pp. 1078108810.1007/s00170-003-1939-9Search in Google Scholar

8 M. C.Çakır, A.Gürarda: Optimization and graphical representation of machining conditions in multi-pass turning operations, Computer Integrated Manufacturing Systems11 (1998), No. 3, pp. 15717010.1061/(ASCE)PS.1949-1204.0000076Search in Google Scholar

9 R.Gupta, J. L.Batra, J. K.Lal: Determination of optimal subdivision of depth of cut in multi-pass turning with constraints, International Journal of Production Research33 (1995), pp. 11512710.1016/S0951-5240(98)00015-9Search in Google Scholar

10 S. E.Kilic, C.Cogun, D. T.Sen: A computer-aided graphical technique for the optimization of machining conditions, Computers in Industry22 (1993), No. 3, pp. 31932610.1016/0166-3615(93)90099-MSearch in Google Scholar

11 Z. G.Wang, M.Rahman, Y. S.Wong, J.Sun: Optimization of multi-pass milling using parallel genetic algorithm parallel genetic simulated annealing, International Journal of Machine Tools & Manufacture45 (2005), No. 15, pp. 1726173410.1016/j.ijmachtools.2005.03.009Search in Google Scholar

12 Z. G.Wang, Y. S.Wong, M.Rahman: Optimisation of multi-pass milling using genetic algorithm genetic simulated annealing, International Journal of Advanced Manufacturing Technology24 (2004), No. 9–10, pp. 72773210.1007/s00170-003-1789-5Search in Google Scholar

13 N. N.: Machining Data Handbook, Vol. 1, 3rd Ed., Machinability Data Center, OH, USA (1980)Search in Google Scholar

14 P. G.Petropoulos: Optimal selection of machining rate variable by geometric programming, International Journal of Production Research11 (1973), No. 4, pp. 30531410.1080/00207547308929981Search in Google Scholar

15 M. T.Rad, I. M.Bidhendi: On the optimization of machining parameters for milling operations, International Journal of Machine Tools & Manufacture37 (1997), No. 1, pp. 11610.1016/S0890-6955(96)00044-2Search in Google Scholar

16 Y. C.Shin, Y. S.Joo: Optimization of machining conditions with practical constraints, International Journal of Production Research30 (1992), No. 12, pp. 2907291910.1080/00207549208948198Search in Google Scholar

17 S.Srisompom, S.Bureerat: Geometrical design of plate-fin heat sinks using hybridization of MOEA and RSM, IEEE Transactions on Components and Packaging Technologies31 (2008), No. 2, pp. 35136010.1109/TCAPT.2008.916799Search in Google Scholar

18 A. R.Yıldız: A comparative study of population-based optimization algorithms for turning operations, Information Sciences210 (2012), pp. 818810.1016/j.ins.2012.03.005Search in Google Scholar

19 J.Wang, T.Kuriyagawa, X. P.Wei, D. M.Guo: Optimization of cutting conditions for single pass turning operations using a deterministic approach, International Journal of Machine Passs and Manufacture42 (2002), No. 9, pp. 1023103310.1016/S0890-6955(02)00037-8Search in Google Scholar

20 J.Wang, J. A.Armarego: Computer-aided optimization of multiple constraint single pass face milling operations, Machining Science and Technology5 (2001), No. 1, pp. 779910.1081/MST-100103179Search in Google Scholar

21 J.Wang: Computer-aided economic optimization of end-milling operations, International Journal of Production Economics54 (1998), No. 3, pp. 30732010.1016/S0925-5273(98)00008-5Search in Google Scholar

22 A. R.Yildiz, K.Solanki: Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach, International Journal of Advanced Manufacturing Technology59 (2012), No. 1–4, pp. 36737610.1007/s00170-011-3496-ySearch in Google Scholar

23 A. R.Yildiz, K.Saitou: Topology synthesis of multi-component structural assemblies in continuum domains, Transactions of ASME, Journal of Mechanical Design133 (2011), No. 1, 01100810.1115/1.4003038Search in Google Scholar

24 A. R.Yildiz: A novel particle swarm optimization approach for product design and manufacturing, International Journal of Advanced Manufacturing Technology40 (2009), No. 5 – 6, pp. 61762810.1007/s00170-008-1453-1Search in Google Scholar

25 A. R.Yildiz: Hybrid immune-simulated annealing algorithm for optimal design and manufacturing, International Journal of Materials and Product Technology34 (2009), No. 3, pp. 21722610.1504/IJMPT.2009.024655Search in Google Scholar

26 A. R.Yildiz: A novel hybrid immune algorithm for global optimization in design and manufacturing, Robotics and Computer-Integrated Manufacturing25 (2009), No. 2, pp. 26127010.1016/j.rcim.2007.08.002Search in Google Scholar

27 A. R.Yildiz: An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry, Journal of Materials Processing Technology50 (2009), No. 4, pp. 22422810.1016/j.jmatprotec.2008.06.028Search in Google Scholar

28 A. R.Yildiz: Comparison of evolutionary-based optimization algorithms for structural design optimization, Engineering Applications of Artificial Intelligence26 (2013), pp. 32733310.1016/j.engappai.2012.05.014Search in Google Scholar

29 B. S.Yildiz: A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems, International Journal of Vehicle Design, 73 (2017), pp. 20821810.1504/IJVD.2017.10003412Search in Google Scholar

30 B. S.Yildiz, H.Lekesiz, A. R.Yildiz: Structural design of vehicle components using gravitational search and charged system search algorithms, Materials Testing58 (2016), No. 1, pp. 798110.3139/120.110819Search in Google Scholar

31 A. R.Yildiz: Optimal structural design of vehicle components using topology design and optimization, Materials Testing50 (2008), No. 4, pp. 22422810.3139/120.100880Search in Google Scholar

32 A. R.Yildiz, N.Kaya, O. B.Alankus, F.Ozturk: Optimal design of vehicle components using topology design and optimization, International Journal of Vehicle Design34 (2004), pp. 38739810.1504/IJVD.2004.004064Search in Google Scholar

33 B. S.Yildiz, H.Lekesiz: Fatigue-based structural optimisation of vehicle components, International Journal of Vehicle Design73 (2017), pp. 546210.1504/IJVD.2017.10003398Search in Google Scholar

34 M.Kiani, A. R.Yildiz: A comparative study of non-traditional methods for vehicle crashworthiness and NVH optimization, Archive and Computational Methods Engineering23 (2016), pp. 72373410.1007/s11831-015-9155-ySearch in Google Scholar

35 N.Pholdee, S.Bureerat, A. R.Yildiz: Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame, International Journal of Vehicle Design73 (2017), pp. 205310.1504/IJVD.2017.10003397Search in Google Scholar

36 S.Mirjalili: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Systems89 (2015), pp. 2282490.1016/j.knosys.2015.07.0Search in Google Scholar

Published Online: 2017-04-29
Published in Print: 2017-05-02

© 2017, Carl Hanser Verlag, München

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