Home Hybrid spotted hyena–Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations
Article
Licensed
Unlicensed Requires Authentication

Hybrid spotted hyena–Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations

  • Nantiwat Pholdee

    Dr. Nantiwat Pholdee, born 1986, received his BEng degree (Second Class Honors) in Mechanical Engineering in 2008 and his Ph.D. degree in Mechanical Engineering in 2013 from Khon Kaen University, Khon Kaen, Thailand. His research interests include multidisciplinary design optimization, aircraft design, flight control, evolutionary computation, and finite-element analysis.

         , Vivek K. Patel

    Dr. Vivek K. Patel, born 1980, received a Ph.D. in the area of thermal system optimization from SVNIT. He acquired more than 12 years of teaching experience. His research area is focused on the development of metaheuristic algorithms for the design and optimization of real-life engineering applications and energy systems. He currently works as an Assistant Professor in the Mechanical Engineering Department of Pandit Deendayal Petroleum University, Gandhinagar.

         , Sadiq M. Sait

    Dr. Sadiq M. Sait, born 1957, received his bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia. He is a Senior Member of the IEEE. In 1981, he received the Best Electronic Engineer Award from the Indian Institute of Electrical Engineers, Bengaluru.

         , Sujin Bureerat

    Dr.Sujin Bureerat, born 1970, received his BEng degree in Mechanical Engineering from Khon Kaen University, Khon Kaen, Thailand, in 1992, and his PhD degree in Engineering from Manchester University, Manchester, UK, in 2001. Currently, he is a Professor with the Department of Mechanical Engineering, Khon Kaen University. His research interests include multidisciplinary design optimization, evolutionary computation, aircraft design, finite-element analysis, agricultural machinery, mechanism synthesis, and mechanical vibration.

         and Ali Rıza Yıldız

    Dr. Ali Rıza Yıldız, born 1978, is a Professor at the Department of Automotive Engineering, Uludağ University, Turkey. His research interests are the finite element analysis of automobile components, lightweight design, composite materials, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

     EMAIL logo
Published/Copyright: March 31, 2021
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Materials Testing
From the journal Materials Testing Volume 63 Issue 3

Abstract

In this research, a novel optimization algorithm, which is a hybrid spotted hyena-Nelder-Mead optimization algorithm (HSHO-NM) algorithm, has been introduced in solving grinding optimization problems. A well-known grinding optimization problem is solved to prove the superiority of the HSHO-NM over other algorithms. The results of the HSHO-NM are compared with others. The results show that HSHO-NM is an efficient optimization approach for obtaining the optimal manufacturing variables in grinding operations.

Keywords: Spotted hyena optimization algorithm; nelder-mead optimization; manufacturing; grinding operation
Prof. Dr. Ali Rıza Yıldız Department of Automotive Engineering Uludağ University Görükle, Bursa, Turkey

About the authors

Dr. Nantiwat Pholdee

Dr. Nantiwat Pholdee, born 1986, received his BEng degree (Second Class Honors) in Mechanical Engineering in 2008 and his Ph.D. degree in Mechanical Engineering in 2013 from Khon Kaen University, Khon Kaen, Thailand. His research interests include multidisciplinary design optimization, aircraft design, flight control, evolutionary computation, and finite-element analysis.

Dr. Vivek K. Patel

Dr. Vivek K. Patel, born 1980, received a Ph.D. in the area of thermal system optimization from SVNIT. He acquired more than 12 years of teaching experience. His research area is focused on the development of metaheuristic algorithms for the design and optimization of real-life engineering applications and energy systems. He currently works as an Assistant Professor in the Mechanical Engineering Department of Pandit Deendayal Petroleum University, Gandhinagar.

Dr. Sadiq M. Sait

Dr. Sadiq M. Sait, born 1957, received his bachelor’s degree in Electronics Engineering from Bangalore University, India, in 1981, and his master’s and Ph.D. degrees in Electrical Engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, in 1983 and 1987, respectively. He is currently a Professor of Computer Engineering and Director of the Center for Communications and IT Research, KFUPM, Dhahran, Saudi Arabia. He is a Senior Member of the IEEE. In 1981, he received the Best Electronic Engineer Award from the Indian Institute of Electrical Engineers, Bengaluru.

Dr. Sujin Bureerat

Dr.Sujin Bureerat, born 1970, received his BEng degree in Mechanical Engineering from Khon Kaen University, Khon Kaen, Thailand, in 1992, and his PhD degree in Engineering from Manchester University, Manchester, UK, in 2001. Currently, he is a Professor with the Department of Mechanical Engineering, Khon Kaen University. His research interests include multidisciplinary design optimization, evolutionary computation, aircraft design, finite-element analysis, agricultural machinery, mechanism synthesis, and mechanical vibration.

Prof. Dr. Ali Rıza Yıldız

Dr. Ali Rıza Yıldız, born 1978, is a Professor at the Department of Automotive Engineering, Uludağ University, Turkey. His research interests are the finite element analysis of automobile components, lightweight design, composite materials, vehicle design, vehicle crashworthiness, shape and topology optimization of vehicle components, meta-heuristic optimization techniques, and additive manufacturing.

References

1 B. S. Yildiz, A. R. Yildiz: Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes, Materials Testing 59 (2017), No. 5, pp. 425-429 DOI:10.3139/120.11102410.3139/120.111024Search in Google Scholar

2 A. R. Yildiz, B. S. Yildiz, S. M. Sait, X. Y. Li: The Harris hawks, grasshopper and multi-verse optimization algorithms for the selection of optimal machining parameters in manufacturing operations, Materials Testing 61 (2019), pp. 725-733 DOI:10.3139/120.11137710.3139/120.111377Search in Google Scholar

3 A. R. Yildiz: A novel hybrid whale nelder mead algorithm for optimization of design and manufacturing problems, International Journal of Advanced Manufacturing Technology 105 (2019), pp. 5091-5104 DOI:10.1007/s00170-019-04532-110.1007/s00170-019-04532-1Search in Google Scholar

4 A. R. Yildiz: A novel hybrid immune algorithm for global optimization in design and manufacturing, Robotics and Computer-Integrated Manufacturing 25 (2009), No. 2, pp. 261-270 DOI:10.1016/j.rcim.2007.08.00210.1016/j.rcim.2007.08.002Search in Google Scholar

5 N. Panagant, N. Pholdee, S. Bureerat, A. R. Yildiz, S. M. Sait: Seagull optimization algorithm for solving real-world design optimization problems, Materials Testing 62 (2020), No. 6, pp. 640-644 DOI: 10.3139/120.11152910.3139/120.111529Search in Google Scholar

6 B. S. Yildiz, N. Pholdee, S. Bureerat, S. M. Sait, A. R. Yildiz: Sine-cosine optimization algorithm for the conceptual design of automobile components, Materials Testing 62 (2020), No. 7, pp. 744-748 DOI: 10.3139/120.11154110.3139/120.111541Search in Google Scholar

7 A. R. Yildiz, F. Ozturk: Hybrid Taguchiharmony search approach for shape optimization, Recent Advances in Harmony Search Algorithm 270 (2010), pp. 89-98 DOI:10.1007/978-3-642-04317-8_810.1007/978-3-642-04317-8_8Search in Google Scholar

8 A. R. Yildiz, N. Kaya, N. Ozturk, F. Ozturk: Hybrid approach for genetic algorithm and Taguchi’s method based design optimization in the automotive industry, International Journal of Production Research 44 (2006), pp. 4897-4914 DOI:10.1080/0020754060061993210.1080/00207540600619932Search in Google Scholar

9 A. R. Yildiz: Designing of optimum vehicle components using new generation optimization methods, Journal of Polytechnic 20 (2017), No. 2, pp. 319-323 DOI:10.2339/2017.20.2 325-33210.2339/2017.20.2325-332Search in Google Scholar

10 A. R. Yildiz, K. Solanki: Multi-objective optimization of vehicle crashworthiness using new particle swarm based approach, International Journal of Advanced Manufacturing Technology 59 (2012), No. 1-4, pp. 367-376 DOI:10.1007/s00170-011-3496-y10.1007/s00170-011-3496-ySearch in Google Scholar

11 A. Baykasoglu, F. B. Ozsoydan, M. E. Senol: Weighted superposition attraction algorithm for binary optimization problems, Operational Research, 20(2020), pp. 2555-2581 DOI:10.1007/s12351-018-0427-910.1007/s12351-018-0427-9Search in Google Scholar

12 B. S. Yıldız: The spotted hyena optimization algorithm for weight-reduction of automobile brake components, Materials Testing 62 (2020), No. 4, pp. 383-388 DOI:10.3139/120.11149510.3139/120.111495Search in Google Scholar

13 B. S. Yıldız, A. R. Yildiz, E. I. Albak, H. Abderazek, Sadiq M. Sait, S. Bureerat: Butterfly optimization algorithm for optimum shape design of automobile suspension components, Materials Testing 62 (2020), No. 4, pp. 365-370 DOI:10.3139/120.11149210.3139/120.111492Search in Google Scholar

14 A. R. Yıldız, U. A. Kılıcarpa, E. Demirci: Topography and topology optimization of diesel engine components for light-weight design in the automotive industry, Materials Testing 61 (2019), No. 1, pp. 27-34 DOI:10.3139/120.11127710.3139/120.111277Search in Google Scholar

15 S. Khalilpourazari, S. Khalilpourazary: A lexicographic weighted Tchebycheff approach for multi-constrained multi-objective optimization of the surface grinding process, Engineering Optimization 49 (2017), No. 5, pp. 878-895 DOI:10.1080/0305215X.2016.121443710.1080/0305215X.2016.1214437Search in Google Scholar

16 E. Bogar, S. Beyhan: Adolescent Identity Search Algorithm (AISA): A novel metaheuristic approach for solving optimization problems, Applied Soft Computing 95 (2020), Article Number 106503 DOI:10.1016/j.asoc.2020.10650310.1016/j.asoc.2020.106503Search in Google Scholar

17 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 & Manufacture 45 (2005), No. 15, pp. 1726-1734 DOI:10.1016/j.ijmachtools.2005.03.00910.1016/j.ijmachtools.2005.03.009Search in Google Scholar

18 W. W. Gilbert: Economics of machining theory and practice, American Society Metals, Cleveland, OH, USA (1950)Search in Google Scholar

19 K. Okushima, K. Hitomi: A study of economic machining: an analysis of maximum profit cutting speed, International Journal of Production Research 3 (1964), pp. 73-78 DOI:10.1080/0020754640894304610.1080/00207546408943046Search in Google Scholar

20 D. S. Ermer: Optimization of the constrained machining economics problem by geometric programming, Journal of Engineering for Industry 93 (1971), No. 4, pp. 1067-1072 DOI:10.1115/1.342804410.1115/1.3428044Search in Google Scholar

21 P. G. Petropoulos: Optimal selection of machining rate variable by geometric programming, International Journal of Production Research 11 (1973), No. 4, pp. 305-314 DOI:10.1080/0020754730892998110.1080/00207547308929981Search in Google Scholar

22 G. Boothroyd, P. Rusek: Maximum rate of profit criteria in machining, Journal of Engineering for Industry 98 (1976), No. 1, pp. 217-220 DOI:10.1115/1.343882210.1115/1.3438822Search in Google Scholar

23 S. K. Hati, S. S. Rao: Determination of optimum machining conditions deterministic probabilistic approaches, Journal of Engineering for Industry 98 (1976), No. 1, pp. 354-359 DOI:10.1115/1.343885310.1115/1.3438853Search in Google Scholar

24 K. Iwata, Y. Murotsu, T. Iwatsubo, F. Oba: Optimization of cutting conditions for multi-pass operations considering probabilistic nature in machining conditions, Journal of Engineering for Industry 99 (1977), No. 1, pp. 211-217 DOI:10.1115/1.343914010.1115/1.3439140Search in Google Scholar

25 B. K. Lambert, A. Walvekar: Optimization of multi pass machining operations, International Journal of Production Research 16 (1978), No. 4, pp. 259-265 DOI:10.1080/0020754780893001810.1080/00207547808930018Search in Google Scholar

26 M. C. Chen, D. M. Tsai: A simulated annealing approach for optimization of multi-pass turning operations, International Journal of Production Research 34 (1996), No. 10, pp. 2803-2825 DOI:10.1080/0020754960890506010.1080/00207549608905060Search in Google Scholar

27 D. S. Ermer, S. Kromodihardo: Optimization of multi pass turning with constraints, Journal of Engineering for Industry 103 (1981), No. 4, pp. 462-468 DOI:10.1115/1.318451310.1115/1.3184513Search in Google Scholar

28 B. Gopalkrishan, F. A. Khayyal: Machining parameter selection for turning with constraints: an analytical approach based on geometric programming, International Journal of Production Research 29 (1991), No. 9, pp. 1897-1908 DOI:10.1080/0020754910894805610.1080/00207549108948056Search in Google Scholar

29 Y. C Shin, Y. S. Joo: Optimization of machining conditions with practical constraints, International Journal of Production Research 30 (1992), No. 12, pp. 2907-2919 DOI:10.1080/0020754920894819810.1080/00207549208948198Search in Google Scholar

30 R. Gupta, J. L. Batra, G. K. Lal: Determination of optimal subdivision of depth of cut in multi-pass turning with constraints, International Journal of Production Research 33 (1995), No. 9, pp. 2555-2565 DOI:10.1080/0020754950890483110.1080/00207549508904831Search in Google Scholar

31 F. P. Tan, R. C. Creese: A generalized multi-pass machining model for machining parameter selection in turning, International Journal of Production Research 33 (1995), No. 5, pp. 1467-1487 DOI:10.1080/0020754950893022110.1080/00207549508930221Search in Google Scholar

32 J. S. Agapiou: The optimisation of machining operations based on a combined criterion Part 2: Multipass operations, Journal of Engineering for Industry 114 (1992), No. 4, pp. 508-513 DOI:10.1115/1.290070510.1115/1.2900705Search in Google Scholar

33 E. J. A. Armarego, A. J. R. Smith, J. Wang: Constrained optimization strategies CAM software for single-pass peripheral milling, International Journal of Production Research 31 (1993), No. 9, pp. 2139-2160 DOI:10.1080/0020754930895684910.1080/00207549308956849Search in Google Scholar

34 E. Demirci, A. R. Yıldız: An investigation of the crash performance of magnesium, aluminum and advanced high strength steels and different cross-sections for vehicle thin-walled energy absorbers, Materials Testing 60 (2018), pp. 661-668 DOI:10.3139/120.11120110.3139/120.111201Search in Google Scholar

35 M. T. Rad, I. M. Bidhendi: On the optimization of machining parameters for milling operations, International Journal of Machine Tools & Manufacture 37 (1997), No. 1, pp. 1-16 DOI:10.1016/S0890-6955(96)00044-210.1016/S0890-6955(96)00044-2Search in Google Scholar

36 P. C. Fourie, A. A. Groenwold: The particle swarm optimization algorithm in size shape optimization, Structural and Multidisciplinary Optimization 23 (2002), No. 4, pp. 259-267 DOI:10.1007/s00158-002-0188-010.1007/s00158-002-0188-0Search in Google Scholar

37 T. Kunakote, S. Bureerat: Multi-objective topology optimization using evolutionary algorithms, Engineering Optimization 43 (2011), No. 5, pp. 541-557 DOI:10.1080/0305215X.2010.50293510.1080/0305215X.2010.502935Search in Google Scholar

38 S. Saikumar, M. S. Shunmugam: Parameter selection based on surface finish in high-speed end-milling using differential evolution, Materials and Manufacturing 21 (2006), No. 4, pp. 341-347 DOI:10.1080/1042691050041154610.1080/10426910500411546Search in Google Scholar

39 K. Vijayakumar, G. Prabhaharan, P. Asokan, R. Saravanan: Optimization of multi-pass turning operation using ant colony system, International Journal of Machine Tools & Manufacture 43 (2003), No. 15, pp. 1633-1639 DOI:10.1016/S0890-6955(03)00081-610.1016/S0890-6955(03)00081-6Search in Google Scholar

40 I. Rajendran, S. Vijayarangan: Optimal design of a composite leaf spring using genetic algorithms, Computers and Structures 79 (2001), No. 11, pp. 1121-1129 DOI:10.1016/S0045-7949(00)00174-710.1016/S0045-7949(00)00174-7Search in Google Scholar

41 F. Hamza, H. Abderazek, S. Lakhdar, D. Ferhat, A. R. Yildiz: Optimum design of cam-roller follower mechanism using a new evolutionary algorithm, The International Journal of Advanced Manufacturing Technology 99 (2018), No. 5-8, pp. 1261-1282 DOI:10.1007/s00170-018-2543-310.1007/s00170-018-2543-3Search in Google Scholar

42 B. S. Yıldız: Optimal design of automobile structures using moth-flame optimization algorithm and response surface methodology, Materials Testing 62 (2020), No. 4, pp. 371-377 DOI:10.3139/120.11149410.3139/120.111494Search in Google Scholar

43 B. Aslan, A. R. Yildiz: Optimum design of automobile components using lattice structures for additive manufacturing, Materials Testing 62 (2020), pp. 633-639 DOI:10.3139/120.11152710.3139/120.111527Search in Google Scholar

44 E. Kurtuluş, A. R. Yildiz, S. Bureerat, Sadiq M. Sait: A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails, Materials Testing 62 (2020), No. 3, pp. 251-260 DOI:10.3139/120.11147810.3139/120.111478Search in Google Scholar

45 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), No. 1-3, pp. 208-218 DOI:10.1504/IJVD.2017.08260310.1504/IJVD.2017.082603Search in Google Scholar

46 E. Demirci, A. R. Yıldız: An experimental and numerical investigation of the effects of geometry and spot welds on the crashworthiness of vehicle thin-walled structures, Materials Testing 60 (2018), No. 6, pp. 553-561 DOI:10.3139/120.11118710.3139/120.111187Search in Google Scholar

47 H. Özkaya, M. Yildiz, A. R. Yildiz, S. Bureerat, B. S. Yildiz, Sadiq M. Sait: The equilibrium optimization algorithm and the response surface based metamodel for optimal structural design of vehicle components, Materials Testing 62 (2020), pp. 492-496 DOI:10.3139/120.11150910.3139/120.111509Search in Google Scholar

48 P. Champasak, N.Panagant, N. Pholdee, S. Bureerat, A. R. Yildiz: Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle, Aerospace Science and Technology 100 (2020), pp. 1-11 DOI:10.1016/j.ast.2020.10578310.1016/j.ast.2020.105783Search in Google Scholar

49 B. S. Yildiz, A. R. Yildiz: Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod, Materials Testing 60 (2018), No. 3, pp. 311-315 DOI:10.3139/120.11115310.3139/120.111153Search in Google Scholar

50 A. R. Yildiz, K. Saitou: Topology synthesis of multi-component structural assemblies in continuum domains, Transactions of ASME, Journal of Mechanical Design 133 (2011), No. 1, 011008-9 DOI:10.1115/1.400303810.1115/1.4003038Search in Google Scholar

51 B. Hekimoglu: Optimal tuning of fractional order pid controller for dc motor speed control via chaotic atom search optimization algorithm, IEEE ACCESS 7 (2019), pp. 38100-38114 DOI:10.1109/ACCESS.2019.290596110.1109/ACCESS.2019.2905961Search in Google Scholar

52 M. C. Chen, K. Y. Chen: Optimization of multi-pass turning operations with genetic algorithms: a note, International Journal of Production Research 41 (2003), No. 14, pp. 3385-3388 DOI:10.1080/002075403100011814310.1080/0020754031000118143Search in Google Scholar

53 T. Güler, A. Demirci, A. R. Yıldız, U. Yavuz: Lightweight design of an automobile hinge component using glass fiber polyamide composites, Materials Testing 60 (2018), No. 3, pp. 306-310 DOI:10.3139/120.11115210.3139/120.111152Search in Google Scholar

54 B. S. Yildiz: Natural frequency optimization of vehicle components using the interior search algorithm, Materials Testing 59 (2017), No. 5, pp. 456-458 DOI:10.3139/120.11101810.3139/120.111018Search in Google Scholar

55 B. S. Yildiz: The mine blast algorithm for the structural optimization of electrical vehicle components, Materials Testing 62 (2020), No. 5, pp. 497-501 DOI:10.3139/120.11151110.3139/120.111511Search in Google Scholar

56 A. Karaduman, B. S. Yıldız, A. R. Yıldız: Experimental and numerical fatigue-based design optimisation of clutch diaphragm spring in the automotive industry, International Journal of Vehicle Design 80 (2020), No. 2-4, pp. 330-345 DOI:10.1504/IJVD.2019.10987510.1504/IJVD.2019.109875Search in Google Scholar

57 B. S. Yildiz, AR. Yildiz, S. Bureerat, N. Pholdee, Sadiq M. Sait, V. Patel: The Henry gas solubility optimization algorithm for optimum structural design of automobile brake components, Materials Testing 62 (2020), No. 3, pp. 261-264 DOI:10.3139/120.11147910.3139/120.111479Search in Google Scholar

58 H. Abderazek, A. R. Yildiz, S. Mirjalili: Comparison of recent optimization algorithms for design optimization of a cam-follower mechanism, Knowledge-Based Systems 191 (2020), No. 105237 DOI:10.1016/j.knosys.2019.10523710.1016/j.knosys.2019.105237Search in Google Scholar

59 H. Abderazek, A. R. Yildiz, S. M. Sait: Optimal design of planetary gear train for automotive transmissions using advanced meta-heuristics, International Journal of Vehicle Design 80 (2019), No. 2-4, pp. 121-136 DOI:10.1504/IJVD.2019.10986210.1504/IJVD.2019.109862Search in Google Scholar

60 B. Alatas: Sports inspired computational intelligence algorithms for global optimization, Artificial Intelligence Review 52 (2019), pp. 1579-1627 DOI:10.1007/s10462-017-9587-x10.1007/s10462-017-9587-xSearch in Google Scholar

61 H. Abderazek, A. R. Yildiz, S. M. Sait: Mechanical engineering design optimisation using novel adaptive differential evolution algorithm, International Journal of Vehicle Design 80 (2019), No. 2-4, pp. 285-329 DOI:10.1504/IJVD.2019.10987310.1504/IJVD.2019.109873Search in Google Scholar

62 N. Panagan, N. Pholdee; K. Wansasueb, S. Bureerat, A. R. Yildiz; S. M. Sait: Comparison of recent algorithms for many-objective optimisation of an automotive floor-frame, International Journal of Vehicle Design 80 (2019), No. 2-4, pp. 176-208 DOI:10.1504/IJVD.2019.10986310.1504/IJVD.2019.109863Search in Google Scholar

63 C. M. Aye, N. Pholdee, A. R. Yildiz, S. Bureerat, S. M. Sait: Multi-surrogate-assisted metaheuristics for crashworthiness optimisation, International Journal of Vehicle Design 80 (2019), No. 2-4, pp. 223-240 DOI:10.1504/IJVD.2019.10986610.1504/IJVD.2019.109866Search in Google Scholar

64 S. Gupta, K. Deep: An opposition-based chaotic grey wolf optimizer for global optimisation tasks, Journal of Experimental & Theoretical Artificial Intelligence 31 (2019), pp. 751-779 DOI:10.1080/0952813X.2018.155471210.1080/0952813X.2018.1554712Search in Google Scholar

65 R. Sarangkum, K. Wansasueb, N. Panagant, N. Pholdee, S. Bureerat, A. R. Yildiz, S. M. Sait: Automated design of aircraft fuselage stiffeners using multiobjective evolutionary optimisation, International Journal of Vehicle Design 80 (2019), No. 2-4, pp. 162-175 DOI:10.1504/IJVD.2019.10986410.1504/IJVD.2019.109864Search in Google Scholar

66 E. Çelik: A powerful variant of symbiotic organisms search algorithm for global optimization, Engineering Applications of Artificial Intelligence 87 (2020), Article Number 103294 DOI:10.1016/j.engappai.2019.10329410.1016/j.engappai.2019.103294Search in Google Scholar

67 S. Gupta, K. Deep: Hybrid sine cosine artificial bee colony algorithm for global optimization and image segmentation, Neural Computing and Applications 32 (2020), pp. 9521-9543 DOI:10.1007/s00521-019-04465-610.1007/s00521-019-04465-6Search in Google Scholar

68 H. Salimi: Stochastic fractal search: a powerful metaheuristic algorithm, Knowledge-Based Systems 75 (2015), pp. 1-18 DOI:10.1016/j.knosys.2014.07.02510.1016/j.knosys.2014.07.025Search in Google Scholar

69 E. Demirci, A. R. Yıldız: A new hybrid approach for reliability-based design optimization of structural components, Materials Testing 61 (2019), pp. 111-119 DOI:10.3139/120.11129110.3139/120.111291Search in Google Scholar

70 H. Eskandar, A. Sadollah, A. Bahreininejad, M. Hamdi: Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems, Computers & Structure 110-111 (2012), pp. 151-166 DOI:10.1016/j.compstruc.2012.07.01010.1016/j.compstruc.2012.07.010Search in Google Scholar

71 S. Gupta, K. Deep, A. P. Engelbrecht: A memory guided sine cosine algorithm for global optimization, Engineering Applications of Artificial Intelligence 93 (2020) DOI:10.1016/j.engappai.2020.10371810.1016/j.engappai.2020.103718Search in Google Scholar

72 S. Carbas: Optimum structural design of spatial steel frames via biogeography-based optimization, Neural Computing & Applications 28 (2017), pp. 1525-1539 DOI:10.1007/s00521-015-2167-610.1007/s00521-015-2167-6Search in Google Scholar

73 X. Chen, W. B. Rowe: Analysis and simulation of the grinding process. part ii: mechanics of grinding, International Journal of Machine Tools and Manufacture 36 (1996), No. 8, pp. 883-896 DOI:10.1016/0890-6955(96)00117-410.1016/0890-6955(96)00117-4Search in Google Scholar

74 X. M. Wen, A. A. O. Tay, A. Y. C. Nee: Microcomputer-based optimization of the surface grinding process, Journal of Materials Processing Technology 29 (1992), No. 1-3, pp. 75-90 DOI:10.1016/0924-0136(92)90426-S10.1016/0924-0136(92)90426-SSearch in Google Scholar

75 R. Saravanan, P. Asokan, M. Sachidanandam: A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations, International Journal of Machine Tools and Manufacture 42 (2002), No. 12, pp. 1327-1334 DOI:10.1016/S0890-6955(02)00074-310.1016/S0890-6955(02)00074-3Search in Google Scholar

76 N. Baskar, R. Saravanan, P. Asokan, G. Prabhaharan: Ants colony algorithm approach for multi-objective optimisation of surface grinding operations, International Journal of Advanced Manufacturing Technology 23 (2004), No. 5-6, pp. 311-317 DOI:10.1007/s00170-002-1533-610.1007/s00170-002-1533-6Search in Google Scholar

77 A. G. Krishna, K. M. Rao: Multi-objective optimisation of surface grinding operations using scatter search approach, International Journal of Advanced Manufacturing Technology 29 (2006), No. 5-6, pp. 475-480 DOI:10.1007/BF0272909910.1007/BF02729099Search in Google Scholar

78 K.-M. Lee, M.-R. Hsu, J.-H. Chou, C.-Yi Guo: Improved differential evolution approach for optimization of surface grinding process, Expert Systems with Applications 38 (2011), No. 5, pp. 5680-5686 DOI:10.1016/j.eswa.2010.10.06710.1016/j.eswa.2010.10.067Search in Google Scholar

79 G. Zhang, M. Liu, J. Li, W. Ming, X. Shao, Y. Huang: Multiobjective optimization for surface grinding process using a hybrid particle swarm optimization algorithm, The International Journal of Advanced Manufacturing Technology 71 (2014), No. 9-12, pp. 1861-1872 DOI:10.1007/s00170-013-5571-z10.1007/s00170-013-5571-zSearch in Google Scholar

80 A. G. Krishna: Retracted: Optimization of surface grinding operations using a differential evolution approach, Journal of Materials Processing Technology 183 (2007), No. 2-3, pp. 202-209 DOI:10.1016/j.jmatprotec.2006.10.01010.1016/j.jmatprotec.2006.10.010Search in Google Scholar

81 X. Lin, H. Li: Enhanced Pareto particle swarm approach for multiobjective optimization of surface grinding process, Second International Symposium on Intelligent Information Technology Application 2 (2008), pp. 618-623 DOI:10.1109/IITA.2008.7510.1109/IITA.2008.75Search in Google Scholar

82 R. Gupta, K. S. Shishodia, G. S. Sekhon: Optimization of grinding process parameters using enumeration method, Journal of Materials Processing Technology 112 (2001), No. 1, pp. 63-67 DOI:10.1016/S0924-0136(01)00546-510.1016/S0924-0136(01)00546-5Search in Google Scholar

83 A. Slowik, J. Slowik: Multi-objective optimization of surface grinding process with the use of evolutionary algorithm with remembered Pareto set, The International Journal of Advanced Manufacturing Technology 37 (2008), No. 7-8, pp. 657-669 DOI:10.1007/s00170-007-1013-010.1007/s00170-007-1013-0Search in Google Scholar

84 P. J. Pawar, R. V. Rao, J. P. Davim: Multiobjective optimization of grinding process parameters using particle swarm optimization algorithm, Materials and Manufacturing Processes 25 (2010), No. 6, pp. 424-431 DOI:10.1080/1042691090312486010.1080/10426910903124860Search in Google Scholar

85 R. V. Rao, P. J. Pawar: Grinding process parameter optimization using non-traditional optimization algorithms, Journal of Engineering Manufacturing 224 (2010), No. 6, pp. 887-898 DOI:10.1243/09544054JEM178210.1243/09544054JEM1782Search in Google Scholar

86 P. J. Pawar, R. V. Rao: Parameter optimization of machining processes using teaching-learning-based optimization algorithm, International Journal of Advanced Manufacturing Technology 67 (2013), No. 5-8, pp. 995-1006 DOI:10.1007/s00170-012-4524-210.1007/s00170-012-4524-2Search in Google Scholar

87 J. Huang, L. Gao, X. Li: An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes, Applied Soft Computing 36 (2015), pp. 349-356 DOI:10.1016/j.asoc.2015.07.03110.1016/j.asoc.2015.07.031Search in Google Scholar

88 S. Khalilpourazari, S. Khalilpourazary: A robust stochastic fractal search approach for optimization of the surface grinding process, Swarm and Evolutionary Computation 38 (2018), pp. 173-186 DOI:10.1016/j.swevo.2017.07.00810.1016/j.swevo.2017.07.008Search in Google Scholar
Published Online: 2021-03-31

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Corrosion testing
  3. Effects of mixed acid solution on bromide epoxy vinyl ester and its glass fiber reinforced composites
  4. Stress corrosion and mechanical properties of zinc coating on 304 stainless steel
  5. Mechanical testing
  6. Eliminating plasticity effects in the measurement of residual stress by using the hole-drilling method
  7. Fatigue testing
  8. Effect of the galvanization process on the fatigue life of high strength steel compression springs
  9. Materials testing for welding and additive manufacturing applications
  10. Wear behavior and microstructure of Fe-C-Si-Cr-B-Ni hardfacing alloys
  11. Analysis of physical and chemical properties
  12. Structural hydroxyl distribution in jadeite grains and the diagenesis mechanism of jadeitite in Myanmar, Guatemala and Russia
  13. Production-oriented testing
  14. Comparison of processing parameter effects during magnetron sputtering and electrochemical anodization of TiO2 nanotubes on ITO/glass and glass substrates
  15. Mechanical Testing
  16. Effect of hydrothermal aging on the mechanical properties of nanocomposite pipes
  17. Wear Testing
  18. Investigation of the friction behavior of plasma spray Mo/NiCrBSi coated brake discs
  19. Component-Oriented Testing and Simulation
  20. Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design
  21. Fatigue Testing
  22. Development and application of load profiles for thermal qualification testing of receptacle automotive connectors
  23. Analysis of physical and chemical properties
  24. Structural and optical properties of pure ZnO and Al/Cu co-doped ZnO semiconductor thin films and electrical characterization of photodiodes
  25. Mechanical testing/Chemical resistance testing
  26. Effect of using different chemically modified breadfruit peel fiber in the reinforcement of LDPE composite
  27. Component-oriented testing and simulation
  28. Hybrid spotted hyena–Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations
https://doi.org/10.1515/mt-2020-0043
Keywords for this article
Spotted hyena optimization algorithm; nelder-mead optimization; manufacturing; grinding operation

Articles in the same Issue

  1. Frontmatter
  2. Corrosion testing
  3. Effects of mixed acid solution on bromide epoxy vinyl ester and its glass fiber reinforced composites
  4. Stress corrosion and mechanical properties of zinc coating on 304 stainless steel
  5. Mechanical testing
  6. Eliminating plasticity effects in the measurement of residual stress by using the hole-drilling method
  7. Fatigue testing
  8. Effect of the galvanization process on the fatigue life of high strength steel compression springs
  9. Materials testing for welding and additive manufacturing applications
  10. Wear behavior and microstructure of Fe-C-Si-Cr-B-Ni hardfacing alloys
  11. Analysis of physical and chemical properties
  12. Structural hydroxyl distribution in jadeite grains and the diagenesis mechanism of jadeitite in Myanmar, Guatemala and Russia
  13. Production-oriented testing
  14. Comparison of processing parameter effects during magnetron sputtering and electrochemical anodization of TiO2 nanotubes on ITO/glass and glass substrates
  15. Mechanical Testing
  16. Effect of hydrothermal aging on the mechanical properties of nanocomposite pipes
  17. Wear Testing
  18. Investigation of the friction behavior of plasma spray Mo/NiCrBSi coated brake discs
  19. Component-Oriented Testing and Simulation
  20. Comparative investigation of the moth-flame algorithm and whale optimization algorithm for optimal spur gear design
  21. Fatigue Testing
  22. Development and application of load profiles for thermal qualification testing of receptacle automotive connectors
  23. Analysis of physical and chemical properties
  24. Structural and optical properties of pure ZnO and Al/Cu co-doped ZnO semiconductor thin films and electrical characterization of photodiodes
  25. Mechanical testing/Chemical resistance testing
  26. Effect of using different chemically modified breadfruit peel fiber in the reinforcement of LDPE composite
  27. Component-oriented testing and simulation
  28. Hybrid spotted hyena–Nelder-Mead optimization algorithm for selection of optimal machining parameters in grinding operations
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 26.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/mt-2020-0043/html
Scroll to top button