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DİFERANSİYEL GELİŞİM ALGORİTMASI KULLANILARAK OTOMOTİV SÜSPANSİYON YAYLARININ OPTİMUM TASARIMI

Yıl 2018, Cilt: 23 Sayı: 3, 207 - 214, 31.12.2018
https://doi.org/10.17482/uumfd.476611

Öz

Otomotiv endüstrisi son otuz yıldır
istikrarlı bir şekilde büyümekte ve sürekli olarak dünyada teknoloji ve üretim
süreçlerini geliştirmek için çalışmalarını devam ettirmektedir. Otomotiv
şirketleri, artan sayıda firma ve gelişen teknolojik ürünler sonucunda müşteri
beklentilerindeki hızlı artış nedeniyle büyük bir rekabetle karşı karşıyadır.
Rekabet edebilmek için otomotiv üreticilerinin, araç ağırlığı, çarpışma güvenliği,
yakıt emisyonları ve araç konforu gibi müşterilerin ve hükümetlerin
beklentilerini karşılaması gerekiyor. Rekabetin sürdürülebilir olmasını
sağlamak için, şirketler daha hassas operasyonlarla daha az işleme maliyeti
gerektiren daha hafif ve daha verimli parçalar tasarlamalıdır. Son zamanlarda,
yakıt tüketimi ve hava kirliliği ile ilgili endişeler bildirilmiştir. Meta-sezgisel
optimizasyon yöntemleri, son yirmi yıl içinde araç bileşeninin optimizasyonu
için yaygın olarak kullanılmaktadır. Meta-sezgisel optimizasyon
yaklaşımlarından biri olan diferansiyel delişim (DE) algoritması araç
süspansiyon sistemlerinde kullanılan yay tasarımının optimizasyonunda
kullanılmıştır. 
Bu çalışmada kullanılan yayın kütlesi % 29.3
oranında azaltılmıştır. Elde edilen veriler DE algoritmasının literatürde yer
alan diğer evrimsel optimizasyon yöntemlerinden,  genetik algoritma, yapay arı kolonisi
algoritması ve parçacık sürüsü algoritmasından daha iyi çözümler sunduğunu
göstermektedir.

Kaynakça

  • Bhateja, A., Agrawal, V.P. and Athre, K. (1996) Total design concept of springs – a novel approach using MADM methodology, In Advances in Mechanical Engineering, 401–424, New Delhi, India: Narosa Publishing House.
  • Birch, T.W. (1988) Automotive Suspension and Steering Systems. Albany, NY: Delmar Publishers.
  • Bureerat, S., Limtragool, J. (2006) Performance enhancement of evolutionary search for structural topology optimisation, Finite Elements in Analysis and Design , 42(6), 547-566.
  • Camp, C.V., Bichon, B.J., Stovall, S.P., (2005), Design of steel frames using ant colony optimization, Asce-Journal of Structural Engineering, 131, 367-525.
  • Deb, K. (1996) Optimization for Engineering Design: Algorithms and Examples, New Delhi, India:Prentice Hall of India Private Ltd.
  • Eberhart, R., Kennedy. J. (1995) A new optimizer using particle swarm theory, In:Proceedings: IEEE Sixth International Symposium on Micro Machine Human Science.
  • Ferhat, E., Erkan, D., Saka, M.P. (2011) Optimum design of cellular beams using harmony search and particle swarm optimizers, Journal of Constructional Steel Research, 67(2), 237-247.
  • Karaboga, D., Basturk, B. (2003) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39, 459–471.
  • Karaboga, D., Basturk, B. (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, Lecture notes in artificial intelligence 4529, Springer-Verlag, Berlin.
  • Karaboga, D., Basturk, B. (2008) On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 8, 687–697.
  • Liu, X. ,Chadda, Y.S. (1993) Automated optimal design of a leaf spring, SAE-933044, 993–998.
  • Perez, R.E., Behdinan, K. (2007) Particle swarm approach for structural design optimization, Computers and Structures, 85, 1579-1588.
  • Pollanen, I. and Martikka, H. (2010) Optimal re-design of helical springs using fuzzy design and FEM, Advances in Engineering Software, 41 (3), 410-414.
  • Rajendran, I., Vijayarangan, S. (2001) Optimal design of a composite leaf spring using genetic algorithms, Computers and Structures, 79, 1121–1129.
  • Sharma, A., Bergaley, A. (2014) Design and Analysis of Composite Leaf Spring – A Review, International Journal of Engineering Trends and Technology, 9(3), 124-128.
  • Storn, R., Price, K. (1995) Differential Evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces Technical Report TR-95-12, International Computer Science, Berkeley, California.
  • Timmins, P.F. (1977) A feasibility study of fiber reinforced plastics for use in the spring industry, IMechE - C258/77, 73–78.
  • Weeton, J.W. (Ed.) (1986) Engineers Guide to Composite Materials. Metals Park, OH: American Society for Metals.
  • Yildiz, A.R., Saitou, K., (2011), Topology Synthesis of Multi-Component Structural Assemblies in Continuum Domains, Transactions of ASME. Journal of Mechanical Design, 133(1), 011008-9.
  • Yildiz, A.R., Solanki, K.N. (2011) Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach, International Journal of Advance Manufacturing Technology. doi: 10.1007/s00170-011-3496-y
  • Yildiz, B.S. (2017) A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems, International Journal of Vehicle Design,73,1-3,208-218.
  • Yildiz, B.S., Lekesiz, H. (2017) Fatigue-based structural4 optimisation of vehicle components, International Journal of Vehicle Design, 73,1-3, 54-62.
  • Yildiz, B.S., Lekesiz, H., Yildiz, A.R. (2016), Structural design of vehicle components using gravitational search and charged system search algorithms, Materials Testing, 58,1,79-81.
  • Zhang, Y., He, X., Liu, Q. and Wen, B. (2005) Reliability-based optimization of automobile components, Int. J. Vehicle Safety, 1, 52–63.

Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm

Yıl 2018, Cilt: 23 Sayı: 3, 207 - 214, 31.12.2018
https://doi.org/10.17482/uumfd.476611

Öz

The automotive industry has been growing
steadily and paying attention to develop
technologies and production processes in the world. Automotive companies are
facing great competition due to the
increasing number of companies and the rapid increase in customer expectations
as a result of developing technological products. In order to compete, automotive manufacturers need to meet the
expectations of customers and governments, such as vehicle weight, collision
safety, fuel emissions and vehicle
comfort. In order to ensure that
competition is sustainable, companies should design lighter and more efficient
parts that require less processing costs with more precise operations.
Recently, concerns about fuel consumption and air pollution have been reported. Meta-heuristic optimization
methods have been widely used for
optimization of vehicle component over the past three decades.  In this paper, differential evolution (DE)
algorithm is used for optimization of the coil spring design, which is one of the
suspension spring types. 
Using the DE algorithm, the mass of the
coil spring decreased by about 29.3 %. The results show that the DE algorithm
provides better solutions as previous methods in the literature. 

Kaynakça

  • Bhateja, A., Agrawal, V.P. and Athre, K. (1996) Total design concept of springs – a novel approach using MADM methodology, In Advances in Mechanical Engineering, 401–424, New Delhi, India: Narosa Publishing House.
  • Birch, T.W. (1988) Automotive Suspension and Steering Systems. Albany, NY: Delmar Publishers.
  • Bureerat, S., Limtragool, J. (2006) Performance enhancement of evolutionary search for structural topology optimisation, Finite Elements in Analysis and Design , 42(6), 547-566.
  • Camp, C.V., Bichon, B.J., Stovall, S.P., (2005), Design of steel frames using ant colony optimization, Asce-Journal of Structural Engineering, 131, 367-525.
  • Deb, K. (1996) Optimization for Engineering Design: Algorithms and Examples, New Delhi, India:Prentice Hall of India Private Ltd.
  • Eberhart, R., Kennedy. J. (1995) A new optimizer using particle swarm theory, In:Proceedings: IEEE Sixth International Symposium on Micro Machine Human Science.
  • Ferhat, E., Erkan, D., Saka, M.P. (2011) Optimum design of cellular beams using harmony search and particle swarm optimizers, Journal of Constructional Steel Research, 67(2), 237-247.
  • Karaboga, D., Basturk, B. (2003) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39, 459–471.
  • Karaboga, D., Basturk, B. (2007) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems, Lecture notes in artificial intelligence 4529, Springer-Verlag, Berlin.
  • Karaboga, D., Basturk, B. (2008) On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 8, 687–697.
  • Liu, X. ,Chadda, Y.S. (1993) Automated optimal design of a leaf spring, SAE-933044, 993–998.
  • Perez, R.E., Behdinan, K. (2007) Particle swarm approach for structural design optimization, Computers and Structures, 85, 1579-1588.
  • Pollanen, I. and Martikka, H. (2010) Optimal re-design of helical springs using fuzzy design and FEM, Advances in Engineering Software, 41 (3), 410-414.
  • Rajendran, I., Vijayarangan, S. (2001) Optimal design of a composite leaf spring using genetic algorithms, Computers and Structures, 79, 1121–1129.
  • Sharma, A., Bergaley, A. (2014) Design and Analysis of Composite Leaf Spring – A Review, International Journal of Engineering Trends and Technology, 9(3), 124-128.
  • Storn, R., Price, K. (1995) Differential Evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces Technical Report TR-95-12, International Computer Science, Berkeley, California.
  • Timmins, P.F. (1977) A feasibility study of fiber reinforced plastics for use in the spring industry, IMechE - C258/77, 73–78.
  • Weeton, J.W. (Ed.) (1986) Engineers Guide to Composite Materials. Metals Park, OH: American Society for Metals.
  • Yildiz, A.R., Saitou, K., (2011), Topology Synthesis of Multi-Component Structural Assemblies in Continuum Domains, Transactions of ASME. Journal of Mechanical Design, 133(1), 011008-9.
  • Yildiz, A.R., Solanki, K.N. (2011) Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach, International Journal of Advance Manufacturing Technology. doi: 10.1007/s00170-011-3496-y
  • Yildiz, B.S. (2017) A comparative investigation of eight recent population-based optimisation algorithms for mechanical and structural design problems, International Journal of Vehicle Design,73,1-3,208-218.
  • Yildiz, B.S., Lekesiz, H. (2017) Fatigue-based structural4 optimisation of vehicle components, International Journal of Vehicle Design, 73,1-3, 54-62.
  • Yildiz, B.S., Lekesiz, H., Yildiz, A.R. (2016), Structural design of vehicle components using gravitational search and charged system search algorithms, Materials Testing, 58,1,79-81.
  • Zhang, Y., He, X., Liu, Q. and Wen, B. (2005) Reliability-based optimization of automobile components, Int. J. Vehicle Safety, 1, 52–63.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Betül Sultan Yıldız

Yayımlanma Tarihi 31 Aralık 2018
Gönderilme Tarihi 31 Ekim 2018
Kabul Tarihi 8 Kasım 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 23 Sayı: 3

Kaynak Göster

APA Yıldız, B. S. (2018). Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 23(3), 207-214. https://doi.org/10.17482/uumfd.476611
AMA Yıldız BS. Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm. UUJFE. Aralık 2018;23(3):207-214. doi:10.17482/uumfd.476611
Chicago Yıldız, Betül Sultan. “Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 23, sy. 3 (Aralık 2018): 207-14. https://doi.org/10.17482/uumfd.476611.
EndNote Yıldız BS (01 Aralık 2018) Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 23 3 207–214.
IEEE B. S. Yıldız, “Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm”, UUJFE, c. 23, sy. 3, ss. 207–214, 2018, doi: 10.17482/uumfd.476611.
ISNAD Yıldız, Betül Sultan. “Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 23/3 (Aralık 2018), 207-214. https://doi.org/10.17482/uumfd.476611.
JAMA Yıldız BS. Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm. UUJFE. 2018;23:207–214.
MLA Yıldız, Betül Sultan. “Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, c. 23, sy. 3, 2018, ss. 207-14, doi:10.17482/uumfd.476611.
Vancouver Yıldız BS. Optimal Design of Automotive Suspension Springs Using Differential Evolution Algorithm. UUJFE. 2018;23(3):207-14.

DUYURU:

30.03.2021- Nisan 2021 (26/1) sayımızdan itibaren TR-Dizin yeni kuralları gereği, dergimizde basılacak makalelerde, ilk gönderim aşamasında Telif Hakkı Formu yanısıra, Çıkar Çatışması Bildirim Formu ve Yazar Katkısı Bildirim Formu da tüm yazarlarca imzalanarak gönderilmelidir. Yayınlanacak makalelerde de makale metni içinde "Çıkar Çatışması" ve "Yazar Katkısı" bölümleri yer alacaktır. İlk gönderim aşamasında doldurulması gereken yeni formlara "Yazım Kuralları" ve "Makale Gönderim Süreci" sayfalarımızdan ulaşılabilir. (Değerlendirme süreci bu tarihten önce tamamlanıp basımı bekleyen makalelerin yanısıra değerlendirme süreci devam eden makaleler için, yazarlar tarafından ilgili formlar doldurularak sisteme yüklenmelidir).  Makale şablonları da, bu değişiklik doğrultusunda güncellenmiştir. Tüm yazarlarımıza önemle duyurulur.

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