Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 6 Sayı: 2, 43 - 50, 29.06.2019

Öz

Kaynakça

  • [1] Savsani et al., “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems”, Computer-Aided Design, 303-315, (2011).
  • [2] Akay B. and Karaboga D., “Artificial bee colony algorithm for large-scale problems and engineering design optimization”, Journal of Intelligent Manufacturing, Volume 23, Issue 4, 1001–1014, August (2012).
  • [3] Rao R. V. and Patel V., “Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms”, Engineering Optimization, Volume 44, 965-983, (2012).
  • [4] Marler R.T. and Arora J.S., Survey of multi-objective optimization methods for engineering, Structural and Multidisciplinary Optimization, Volume 26, 369–395 (2004).
  • [5] Mendi F., Başkal T., Boran K. and. Boran F. E, “Optimization of module, shaft diameter and rolling bearing for spur gear through genetic algorithm”, Expert Systems with Applications, Volume 37, 8058-8064, December (2010).
  • [6] He S., Prempain E. and Wu Q.H.., “An improved particle swarm optimizer for mechanical design optimization problems” ,Engineering Optimization, 36(5):585–605, (2004).
  • [7] https://fidjit.sg/2017/07/17/guide-different-types-fidget-spinner-bearings/, (2019).
  • [8] Sobel D., Longitude (London: Fourth Estate) p. 103, (1995).
  • [9] Asimow M., Introduction to Engineering Design, McGraw Hill, New York, (1966).
  • [10] Seireg A. and Ezzat H., “Optimum Design of Hydrodynamic Journal Bearings”, ASME J. Lubr. Technol., 91, 516–523, (1969).
  • [11] Maday C. J., “The Maximum Principle Approach to the Optimum One-Dimensional Journal Bearing”, ASME J. Lubr. Technol., 92 (2), 482–489, (1970).
  • [12] Wylie G. M. and Maday C. J., “The Optimum One-Dimensional Hydrodynamic Gas Rayleigh Step Bearing”, ASME J. Lubr. Technol., 92 (3), 504–508, (1970).
  • [13] A. Seireg, “A Survey of Optimization of Mechanical Design”, J. Eng. Ind 94(2), 495-499, May, (1972).
  • [14] Changsen W., “Analysis of Rolling Element Bearings”, Mechanical Engineering Publications Ltd, London, (1991).
  • [15] Hirani H., Athre K. and Biswas S., “Comprehensive design methodology for an engine journal bearing”, Proceedings of Institution of Mechanical Engineers, Part J 214, 401–412, (2000).
  • [16] Goldberg and David, “Genetic Algorithms in Search, Optimization and Machine Learning. Reading”, MA: Addison-Wesley Professional, (1989).
  • [17] Khachaturyan A., Semenovskaya S., and Vainshtein B., “Statistical-Thermodynamic Approach to Determination of Structure Amplitude Phases”, Sov.Phys. Crystallography. 24 (5): 519–524, (1979).
  • [18] Eberhart R. C. and Kennedy J., “A new optimizer using particle swarm theory”, Proc. Sixth Intl. Symp. on Micro Machine and Human Science (Nagoya, Japan), IEEE Service Center, Piscataway, NJ, 39–43, (1995).
  • [19] Dorigo M., “Optimization, Learning and Natural Algorithms”, PhD thesis, Politecnico di Milano, Italy, (1992).
  • [20] Zadeh, L.A. "Fuzzy sets". Information and Control. 8 (3): 338–353, (1965).
  • [21] . Choi D.H and. Yoon K.C, “A design method of an automotive wheel bearing unit with discrete design variables using genetic algorithms”, Transactions of ASME, Journal of Tribology 123 (1) 181–187, (2001).
  • [22] Kalita K., Tiwari R. and. Kakoty S.K, “Multi-Objective Optimisation in rolling element bearing system design”, Proceedings of the International Conference on Optimisation SIGOPT, 17–22, (2002).
  • [23] Chakraborthy I., Vinay K., Nair S.B. and Tiwari R.., “Rolling element bearing design through genetic algorithms”, Engineering Optimisation 35 (6), 649–659, (2003).
  • [24] Shantanu G., Rajiv T. and Shivashankar B. N., “Multi-objective design optimisation of rolling bearings using genetic algorithms”, Mechanism and Machine Theory 42, 1418–1443, (2007).
  • [25] Rao J. S. and Tiwari R., “Design Optimization of Thrust Magnetic Bearings Using Genetic Algorithms”, 7th IFToMM-Conference on Rotor Dynamics, Vienna, Austria, (2006).
  • [26] Rao B. R. and Tiwari R., “Optimum design of rolling element bearings using genetic algorithms”, Mechanism and Machine Theory, 42(2), 233–250, (2007).
  • [27] Kumar S.K., Tiwari R. and Reddy R.S., “Development of an optimumdesign methodology of cylindrical roller bearing using genetic algorithms”, Int. J. Comput.MethodsEng. Sci. Mech. 9 (6) 321–341, (2008).
  • [28] . Kumar S.K, Tiwari R. and Prasad P.V.V.N., “An optimum design of crowned cylindrical roller bearings using genetic algorithms”, Trans. ASME J. Mech. Des. 131 (5), (2009).
  • [29] Wei Y. and Chengzu R., “Optimal Design of High Speed Angular Contact Ball Bearing Using a Multiobjective Evolution Algorithm”, 2010 International Conference on Computing, Control and Industrial Engineering, June 5–6, China, (2010).
  • [30] Tiwari R., Sunil K.K. and Reddy R.S., “An optimal design methodology of tapered roller bearings using genetic algorithms”, Int. J. Comput.Methods Eng. Sci.Mech., 13 (2),108–127, (2012).
  • [31] Waghole and Tiwari R., “Optimization of needle roller bearing design using novel hybrid methods”, Mechanism and Machine Theory, volume 72, 71–85, (2014).
  • [32] Panda, S.N. Panda, P. Nanda and D. Mishra, “Comparative Study on Optimum Design of Rolling Element Bearing”, Tribology International, (2015).
  • [33] Eugenio D., “Optimal design of tapered roller bearings for maximum rating life under combined loads”, Mechanics & Industry 18, 112, (2017).
  • [34] Husain B. and Avinash G., “Optimization of Dynamic Load Carrying Capacity of Deep Groove Ball Bearing using Jaya Algorithm”, International conference on Advances in Thermal Systems, Materials and Design Engineering, (2017).
  • [35] Ashish J. and Rajiv T., “Multi-objective optimization of spherical roller bearings based on fatigue and wear using evolutionary algorithm, Journal of King Saud University”, Engineering Sciences, (2018).
  • [36] Dandagwhal and Kalyankar V. D., “Design Optimization of Rolling Element Bearings Using Advanced Optimization Technique”, Arabian Journalfor Science and Engineering, (2017).

Design Optimisation of Rolling Element Bearings: A Literature Review

Yıl 2019, Cilt: 6 Sayı: 2, 43 - 50, 29.06.2019

Öz

Optimisation is the process of finding the best design parameters that meet engineering needs and provides an inexpensive and flexible tool to define optimal designs to the industry prior to physical application. Engineers use empirical studies, statistical methods and optimisation techniques to evaluate research and determine the best design. In recent years, optimisation studies in the field of engineering has become an area of intensive work. The design optimisation of the bearings is one of these studies. In the design of bearings, there are various constraints such as geometric, kinematic, power, performance, long life and high reliability. An optimal design methodology is needed to perform these constraints collectively. In the literature, there are studies in which conventional and intelligent optimisation techniques are used for the optimisation of bearings. In this article, a source research is carried out, which includes the studies for the optimisation of bearings. 

Kaynakça

  • [1] Savsani et al., “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems”, Computer-Aided Design, 303-315, (2011).
  • [2] Akay B. and Karaboga D., “Artificial bee colony algorithm for large-scale problems and engineering design optimization”, Journal of Intelligent Manufacturing, Volume 23, Issue 4, 1001–1014, August (2012).
  • [3] Rao R. V. and Patel V., “Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms”, Engineering Optimization, Volume 44, 965-983, (2012).
  • [4] Marler R.T. and Arora J.S., Survey of multi-objective optimization methods for engineering, Structural and Multidisciplinary Optimization, Volume 26, 369–395 (2004).
  • [5] Mendi F., Başkal T., Boran K. and. Boran F. E, “Optimization of module, shaft diameter and rolling bearing for spur gear through genetic algorithm”, Expert Systems with Applications, Volume 37, 8058-8064, December (2010).
  • [6] He S., Prempain E. and Wu Q.H.., “An improved particle swarm optimizer for mechanical design optimization problems” ,Engineering Optimization, 36(5):585–605, (2004).
  • [7] https://fidjit.sg/2017/07/17/guide-different-types-fidget-spinner-bearings/, (2019).
  • [8] Sobel D., Longitude (London: Fourth Estate) p. 103, (1995).
  • [9] Asimow M., Introduction to Engineering Design, McGraw Hill, New York, (1966).
  • [10] Seireg A. and Ezzat H., “Optimum Design of Hydrodynamic Journal Bearings”, ASME J. Lubr. Technol., 91, 516–523, (1969).
  • [11] Maday C. J., “The Maximum Principle Approach to the Optimum One-Dimensional Journal Bearing”, ASME J. Lubr. Technol., 92 (2), 482–489, (1970).
  • [12] Wylie G. M. and Maday C. J., “The Optimum One-Dimensional Hydrodynamic Gas Rayleigh Step Bearing”, ASME J. Lubr. Technol., 92 (3), 504–508, (1970).
  • [13] A. Seireg, “A Survey of Optimization of Mechanical Design”, J. Eng. Ind 94(2), 495-499, May, (1972).
  • [14] Changsen W., “Analysis of Rolling Element Bearings”, Mechanical Engineering Publications Ltd, London, (1991).
  • [15] Hirani H., Athre K. and Biswas S., “Comprehensive design methodology for an engine journal bearing”, Proceedings of Institution of Mechanical Engineers, Part J 214, 401–412, (2000).
  • [16] Goldberg and David, “Genetic Algorithms in Search, Optimization and Machine Learning. Reading”, MA: Addison-Wesley Professional, (1989).
  • [17] Khachaturyan A., Semenovskaya S., and Vainshtein B., “Statistical-Thermodynamic Approach to Determination of Structure Amplitude Phases”, Sov.Phys. Crystallography. 24 (5): 519–524, (1979).
  • [18] Eberhart R. C. and Kennedy J., “A new optimizer using particle swarm theory”, Proc. Sixth Intl. Symp. on Micro Machine and Human Science (Nagoya, Japan), IEEE Service Center, Piscataway, NJ, 39–43, (1995).
  • [19] Dorigo M., “Optimization, Learning and Natural Algorithms”, PhD thesis, Politecnico di Milano, Italy, (1992).
  • [20] Zadeh, L.A. "Fuzzy sets". Information and Control. 8 (3): 338–353, (1965).
  • [21] . Choi D.H and. Yoon K.C, “A design method of an automotive wheel bearing unit with discrete design variables using genetic algorithms”, Transactions of ASME, Journal of Tribology 123 (1) 181–187, (2001).
  • [22] Kalita K., Tiwari R. and. Kakoty S.K, “Multi-Objective Optimisation in rolling element bearing system design”, Proceedings of the International Conference on Optimisation SIGOPT, 17–22, (2002).
  • [23] Chakraborthy I., Vinay K., Nair S.B. and Tiwari R.., “Rolling element bearing design through genetic algorithms”, Engineering Optimisation 35 (6), 649–659, (2003).
  • [24] Shantanu G., Rajiv T. and Shivashankar B. N., “Multi-objective design optimisation of rolling bearings using genetic algorithms”, Mechanism and Machine Theory 42, 1418–1443, (2007).
  • [25] Rao J. S. and Tiwari R., “Design Optimization of Thrust Magnetic Bearings Using Genetic Algorithms”, 7th IFToMM-Conference on Rotor Dynamics, Vienna, Austria, (2006).
  • [26] Rao B. R. and Tiwari R., “Optimum design of rolling element bearings using genetic algorithms”, Mechanism and Machine Theory, 42(2), 233–250, (2007).
  • [27] Kumar S.K., Tiwari R. and Reddy R.S., “Development of an optimumdesign methodology of cylindrical roller bearing using genetic algorithms”, Int. J. Comput.MethodsEng. Sci. Mech. 9 (6) 321–341, (2008).
  • [28] . Kumar S.K, Tiwari R. and Prasad P.V.V.N., “An optimum design of crowned cylindrical roller bearings using genetic algorithms”, Trans. ASME J. Mech. Des. 131 (5), (2009).
  • [29] Wei Y. and Chengzu R., “Optimal Design of High Speed Angular Contact Ball Bearing Using a Multiobjective Evolution Algorithm”, 2010 International Conference on Computing, Control and Industrial Engineering, June 5–6, China, (2010).
  • [30] Tiwari R., Sunil K.K. and Reddy R.S., “An optimal design methodology of tapered roller bearings using genetic algorithms”, Int. J. Comput.Methods Eng. Sci.Mech., 13 (2),108–127, (2012).
  • [31] Waghole and Tiwari R., “Optimization of needle roller bearing design using novel hybrid methods”, Mechanism and Machine Theory, volume 72, 71–85, (2014).
  • [32] Panda, S.N. Panda, P. Nanda and D. Mishra, “Comparative Study on Optimum Design of Rolling Element Bearing”, Tribology International, (2015).
  • [33] Eugenio D., “Optimal design of tapered roller bearings for maximum rating life under combined loads”, Mechanics & Industry 18, 112, (2017).
  • [34] Husain B. and Avinash G., “Optimization of Dynamic Load Carrying Capacity of Deep Groove Ball Bearing using Jaya Algorithm”, International conference on Advances in Thermal Systems, Materials and Design Engineering, (2017).
  • [35] Ashish J. and Rajiv T., “Multi-objective optimization of spherical roller bearings based on fatigue and wear using evolutionary algorithm, Journal of King Saud University”, Engineering Sciences, (2018).
  • [36] Dandagwhal and Kalyankar V. D., “Design Optimization of Rolling Element Bearings Using Advanced Optimization Technique”, Arabian Journalfor Science and Engineering, (2017).
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Metalurji ve Malzeme Mühendisliği
Yazarlar

İsmail Şahin 0000-0001-8566-3433

Tolgahan Şahin

Yayımlanma Tarihi 29 Haziran 2019
Gönderilme Tarihi 3 Nisan 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 6 Sayı: 2

Kaynak Göster

APA Şahin, İ., & Şahin, T. (2019). Design Optimisation of Rolling Element Bearings: A Literature Review. Gazi University Journal of Science Part A: Engineering and Innovation, 6(2), 43-50.