Araştırma Makalesi
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Wheel Rim Design Parametrization and Its Effect on Optimization

Yıl 2022, Cilt: 24 Sayı: 72, 913 - 926, 19.09.2022

Yusuf Burak Özdemir , Yalçın Karpuzcu , Serhat Çam , Erkan Günpınar

https://doi.org/10.21205/deufmd.2022247220

Öz

The wheel rims are an essential part of the car. The wheels carry the load of the car and its passengers. To carry this load and prevent loss of life in a possible accident, it is necessary and crucial for the wheel to be strong. Additionally, a wheel rim should also be aesthetically pleasing for customers. First of all, design specifications of the rim model are determined. A user study is then conducted, in which each participants create a detailed wheel rim model from a given conceptual model and parametrizes it using design parameters such as spoke number/shape and hub thickness. In a generative design step, participants then generate 20 distinct models using design parameters. The finite element method (FEM) under stationary car forces is then established to find the stress and displacement distribution. The models are next ranked according to the aesthetic scores (given by a volunteer having mechanical design experience) and stress/displacement values (obtained from the FEM analysis). After such sorting, distinct and aesthetic models were obtained using a genetic algorithm (GA). The participant(s) then select the best model(s) among the new models obtained from GA. Two different wheel rim models obtained during the user study are utilized in a GA-based optimization process. According to the optimization results, parametrization highly affects the aesthetic and mechanical performance of the obtained designs.

Anahtar Kelimeler

Design Parametrization Finite Element Method Genetic Algorithm Design Optimization

Kaynakça

  • Stearns, J., Srivatsan, T., Gao, X., & Lam, P. C., 2006: Understanding the Influence of Pressure And Radial Loads on Stress and Displacement Response of A Rotating Body: The Automobile Wheel. International Journal of Rotating Machinery, 1–8. https://doi.org/10.1155/IJRM/2006/60193
  • Jape, R. K., & Jadhav, S. G., 2016: CAD Modeling and FEA Analysis of Wheel Rim for Weight Reduction. International Journal of Engineering Science and Computing,6(6),7404–7411. https://doi.org/doi.org/10.4010/2016.1756
  • Ayran, E., & Pekedis, M., 2020: Alüminyum Alaşımlı Otomobil Jantlarında Deneysel Darbe Testlerinin Sonlu Elemanlar Yöntemiyle Doğrulanması. DÜMF Mühendislik Dergisi, 11(2), 663–670. https://doi.org/10.24012/dumf.651318
  • Cosseron, K., Mellé, D., Hild, F., & Roux, S., 2019: Optimal Parameterization of Tire–Rim Interaction for Aircraft Wheels. Journal of Aircraft, 56(5), 2032–2046. https://doi.org/10.2514/1.C035343
  • Gondhali, S. L., Dhale, A. D., & Pagare, S. (2019). Static Structural Analysis of Car Rim by Finite Element Method. In Lecture Notes in Mechanical Engineering. https://doi.org/10.1007/978-981-13-2490-1_17
  • Zimmermann, L., Chen, T., & Shea, K., 2017: Design Computing and Cognition’16. In J. S. Gero (Ed.), Design Computing and Cognition’16. https://doi.org/10.1007/978-3-319-44989-0
  • Sureddi, C., 2018: Design, Material Optimization and Dynamic Analysis on Automobile Wheel Rim. International Journal of Scientific and Research Publications (IJSRP), 8(11), 486–509. https://doi.org/10.29322/IJSRP.8.11.2018.p8353
  • J. Wu, X. Qian, M. Y. Wang, Advances in generative design, Computer- Aided Design 116 (2019) 102733. https://doi.org/10.1016/j.cad.2019.102733
  • K. Dorst, N. Cross, Creativity in the design process: co-evolution of problem–solution, Design studies 22 (5) (2001) 425–437. DOI:10.1016/S0142-694X(01)00009-6
  • S. Krish, A practical generative design method, Computer-Aided Design 43 (1) (2011) 88–100. DOI: 10.1016/j.cad.2010.09.009
  • E. Gunpinar, S. E. Ovur, S. Gunpinar, A user-centered side silhouette gen- eration system for sedan cars based on shape templates, Optimization and Engineering 20 (2019) 683–723. DOI: 10.1007/s11081-018-9410-9
  • E. Gunpinar, U. C. Coskun, M. Ozsipahi, S. Gunpinar, A generative design and drag coefficient prediction system for sedan car side silhouettes based on computational fluid dynamics, Computer-Aided Design 111 (2019) 65– 79. DOI: 10.1016/j.cad.2019.02.003
  • E. Gunpinar, S. Khan, A multi-criteria based selection method using non- dominated sorting for genetic algorithm based design, Optimization and Engineering 21 (4) (2020) 1319–1357. https://doi.org/10.1007/s11081-019-09477-8
  • R. H. Kazi, T. Grossman, H. Cheong, A. Hashemi, G. Fitzmaurice, Dreams- ketch: Early stage 3d design explorations with sketching and generative design, in: Proceedings of the 30th Annual ACM Symposium on User In- terface Software and Technology, ACM, 2017, pp. 401–414.
  • E. Gunpinar, S. Gunpinar, A shape sampling technique via particle tracing for cad models, Graphical Models 96 (2018) 11–29. DOI: 10.1016/j.gmod.2018.01.003
  • S. Khan, E. Gunpinar, Sampling cad models via an extended teaching–learning-based optimization technique, Computer-Aided Design 100 (2018) 52–67. DOI: 10.1016/j.cad.2018.03.003
  • J. P. Sousa, J. P. Xavier, Symmetry-based generative design and fabrication: A teaching experiment, Automation in Construction 51 (2015) 113–123. https://doi.org/10.1016/j.autcon.2014.11.001
  • K. M. Dogan, H. Suzuki, E. Gunpinar, M. Kim, A generative sampling sys- tem for profile designs with shape constraints and user evaluation, Comput. Aided Des. 111 (2019) 93–112. DOI: 10.1016/j.cad.2019.02.002
  • S. Khan, E. Gunpinar, M. Moriguchi, H. Suzuki, Evolving a Psycho- Physical Distance Metric for Generative Design Exploration of Diverse Shapes, Journal of Mechanical Design 141 (11). DOI: 10.1115/1.4043678
  • A. Runions, M. Fuhrer, B. Lane, P. Federl, A.-G. Rolland-Lagan, P. Prusinkiewicz, Modeling and visualization of leaf venation patterns, ACM Transactions on Graphics (TOG) 24 (3) (2005) 702–711. https://doi.org/10.1145/1073204.1073251
  • K. Shea, R. Aish, M. Gourtovaia, Towards integrated performance-driven generative design tools, Automation in Construction 14 (2) (2005) 253– 264. DOI: 10.1016/j.autcon.2004.07.002
  • M. Turrin, P. von Buelow, R. Stouffs, Design explorations of performance driven geometry in architectural design using parametric modeling and ge- netic algorithms, Advanced Engineering Informatics 25 (4) (2011) 656– 675. DOI:10.1016/j.aei.2011.07.009
  • S. Khan, E. Gunpinar, B. Sener, Genyacht: An interactive generative de- sign system for computer-aided yacht hull design, Ocean Engineering 191 (2019) 106462. https://doi.org/10.1016/j.oceaneng.2019.106462
  • S. Khan, E. Gunpinar, K. Mert Dogan, A novel design framework for gener- ation and parametric modification of yacht hull surfaces, Ocean Engineer- ing 136 (2017) 243–259. DOI: 10.1016/j.oceaneng.2017.03.013
  • J. J. L. Kitchley, A. Srivathsan, Generative methods and the design process: A design tool for conceptual settlement planning, Applied Soft Computing 14 (2014) 634–652. DOI:10.1016/j.asoc.2013.08.017
  • L. Caldas, Generation of energy-efficient architecture solutions applying gene arch: An evolution-based generative design system, Advanced Engi- neering Informatics 22 (1) (2008) 59–70. DOI: 10.1016/j.aei.2007.08.012
  • S. Khan, M. J. Awan, A generative design technique for exploring shape variations, Advanced Engineering Informatics 38 (2018) 712–724. https://doi.org/10.1016/j.aei.2018.10.005
  • Jiang, X., Lyu, R., Fukushima, Y., Otake, M., & Ju, D. Y., 2018: Lightweight Design and Analysis of Automobile Wheel Based on Bending and Radial Loads. IOP Conference Series: Materials Science and Engineering,372(1). https://doi.org/10.1088/1757899X/372/1/012048
  • Dede, G., Yıldızhan, Ş., Ökten, K., Çalık, A., Uludamar, E., & Özcanlı, M., 2017: Investigation of Stress and Displacement Distribution in Advanced Steel Rims. International Journal of Automotive Engineering and Technologies, 34–37. https://dergipark.org.tr/tr/pub/ijaet/issue/37926/438140

Jant Tasarım Parametrizasyonu ve Parametrizasyonun Optimizasyona Etkisi

Yıl 2022, Cilt: 24 Sayı: 72, 913 - 926, 19.09.2022

Yusuf Burak Özdemir , Yalçın Karpuzcu , Serhat Çam , Erkan Günpınar

https://doi.org/10.21205/deufmd.2022247220

Öz

Jantlar arabanın önemli bir parçasıdır ve tekerlekler ile birlikte arabanın ve yolcularının yükünü taşırlar. Bu yükü taşımak ve olası bir kazada can kaybını önlemek için jantın sağlam olması gerekli ve önemlidir. Diğer taraftan estetik açıdan da göze hitap etmelidir. Bu çalışmada öncelikle araba jantının sınır koşulları belirlenmiştir. Bu sınırlar içerisinde farklı jant tasarımları elde edebilmek için bir kullanıcı çalışması gerçekleştirilmiştir. Kullanıcı çalışmasındaki her bir katılımcı bir model tasarlamış ve parametrize etmiştir. Jant telinin sayısı, şekli ve göbek kalınlığı gibi tasarım parametreleri kullanıcı tarafından belirtilmiştir. Sonrasında kullanıcılardan bu parametreler kullanarak jeneratif tasarım yoluyla birbirinden farklı 20 tane jant modeli elde etmeleri istenmiştir. Durağan arabanın etki ettiği kuvvetler altında (parametrik olarak elde edilen) jantlar modellerinin gerilme ve yer değiştirme dağılımını bulmak için sonlu elemanlar yöntemi (FEM) kullanılmıştır. FEM kullanırken, ağ elemanlarının sayısına ve analiz edilen jantın yönüne dikkat edilmiştir. Jantlar tasarım kabiliyetine sahip gönüllü birisinin verdiği estetik puanlara ve FEM testlerinden elde edilen stres ve yer değiştirme değerlerine göre sıralanmıştır. Sıralamanın ardından genetik algoritma (GA) kullanılarak farklı ve estetik modeller elde edilip, kullanıcıya sunulmuş ve seçimi ile en uygun jant tasarım(lar)ı elde edilmiştir. Bu optimizasyon çalışması parametrizasyonu yapılmış iki farklı jant modeli kullanılarak yapılmıştır. Sonuçlar incelendiğinde parametrizasyon optimizasyon sonrası elde edilen modellerin performanslarını etkilemektedir.

Anahtar Kelimeler

Tasarım Parametrizasyonu Sonlu Elemanlar Yöntemi Genetik Algoritma Tasarım Optimizasyonu

Kaynakça

  • Stearns, J., Srivatsan, T., Gao, X., & Lam, P. C., 2006: Understanding the Influence of Pressure And Radial Loads on Stress and Displacement Response of A Rotating Body: The Automobile Wheel. International Journal of Rotating Machinery, 1–8. https://doi.org/10.1155/IJRM/2006/60193
  • Jape, R. K., & Jadhav, S. G., 2016: CAD Modeling and FEA Analysis of Wheel Rim for Weight Reduction. International Journal of Engineering Science and Computing,6(6),7404–7411. https://doi.org/doi.org/10.4010/2016.1756
  • Ayran, E., & Pekedis, M., 2020: Alüminyum Alaşımlı Otomobil Jantlarında Deneysel Darbe Testlerinin Sonlu Elemanlar Yöntemiyle Doğrulanması. DÜMF Mühendislik Dergisi, 11(2), 663–670. https://doi.org/10.24012/dumf.651318
  • Cosseron, K., Mellé, D., Hild, F., & Roux, S., 2019: Optimal Parameterization of Tire–Rim Interaction for Aircraft Wheels. Journal of Aircraft, 56(5), 2032–2046. https://doi.org/10.2514/1.C035343
  • Gondhali, S. L., Dhale, A. D., & Pagare, S. (2019). Static Structural Analysis of Car Rim by Finite Element Method. In Lecture Notes in Mechanical Engineering. https://doi.org/10.1007/978-981-13-2490-1_17
  • Zimmermann, L., Chen, T., & Shea, K., 2017: Design Computing and Cognition’16. In J. S. Gero (Ed.), Design Computing and Cognition’16. https://doi.org/10.1007/978-3-319-44989-0
  • Sureddi, C., 2018: Design, Material Optimization and Dynamic Analysis on Automobile Wheel Rim. International Journal of Scientific and Research Publications (IJSRP), 8(11), 486–509. https://doi.org/10.29322/IJSRP.8.11.2018.p8353
  • J. Wu, X. Qian, M. Y. Wang, Advances in generative design, Computer- Aided Design 116 (2019) 102733. https://doi.org/10.1016/j.cad.2019.102733
  • K. Dorst, N. Cross, Creativity in the design process: co-evolution of problem–solution, Design studies 22 (5) (2001) 425–437. DOI:10.1016/S0142-694X(01)00009-6
  • S. Krish, A practical generative design method, Computer-Aided Design 43 (1) (2011) 88–100. DOI: 10.1016/j.cad.2010.09.009
  • E. Gunpinar, S. E. Ovur, S. Gunpinar, A user-centered side silhouette gen- eration system for sedan cars based on shape templates, Optimization and Engineering 20 (2019) 683–723. DOI: 10.1007/s11081-018-9410-9
  • E. Gunpinar, U. C. Coskun, M. Ozsipahi, S. Gunpinar, A generative design and drag coefficient prediction system for sedan car side silhouettes based on computational fluid dynamics, Computer-Aided Design 111 (2019) 65– 79. DOI: 10.1016/j.cad.2019.02.003
  • E. Gunpinar, S. Khan, A multi-criteria based selection method using non- dominated sorting for genetic algorithm based design, Optimization and Engineering 21 (4) (2020) 1319–1357. https://doi.org/10.1007/s11081-019-09477-8
  • R. H. Kazi, T. Grossman, H. Cheong, A. Hashemi, G. Fitzmaurice, Dreams- ketch: Early stage 3d design explorations with sketching and generative design, in: Proceedings of the 30th Annual ACM Symposium on User In- terface Software and Technology, ACM, 2017, pp. 401–414.
  • E. Gunpinar, S. Gunpinar, A shape sampling technique via particle tracing for cad models, Graphical Models 96 (2018) 11–29. DOI: 10.1016/j.gmod.2018.01.003
  • S. Khan, E. Gunpinar, Sampling cad models via an extended teaching–learning-based optimization technique, Computer-Aided Design 100 (2018) 52–67. DOI: 10.1016/j.cad.2018.03.003
  • J. P. Sousa, J. P. Xavier, Symmetry-based generative design and fabrication: A teaching experiment, Automation in Construction 51 (2015) 113–123. https://doi.org/10.1016/j.autcon.2014.11.001
  • K. M. Dogan, H. Suzuki, E. Gunpinar, M. Kim, A generative sampling sys- tem for profile designs with shape constraints and user evaluation, Comput. Aided Des. 111 (2019) 93–112. DOI: 10.1016/j.cad.2019.02.002
  • S. Khan, E. Gunpinar, M. Moriguchi, H. Suzuki, Evolving a Psycho- Physical Distance Metric for Generative Design Exploration of Diverse Shapes, Journal of Mechanical Design 141 (11). DOI: 10.1115/1.4043678
  • A. Runions, M. Fuhrer, B. Lane, P. Federl, A.-G. Rolland-Lagan, P. Prusinkiewicz, Modeling and visualization of leaf venation patterns, ACM Transactions on Graphics (TOG) 24 (3) (2005) 702–711. https://doi.org/10.1145/1073204.1073251
  • K. Shea, R. Aish, M. Gourtovaia, Towards integrated performance-driven generative design tools, Automation in Construction 14 (2) (2005) 253– 264. DOI: 10.1016/j.autcon.2004.07.002
  • M. Turrin, P. von Buelow, R. Stouffs, Design explorations of performance driven geometry in architectural design using parametric modeling and ge- netic algorithms, Advanced Engineering Informatics 25 (4) (2011) 656– 675. DOI:10.1016/j.aei.2011.07.009
  • S. Khan, E. Gunpinar, B. Sener, Genyacht: An interactive generative de- sign system for computer-aided yacht hull design, Ocean Engineering 191 (2019) 106462. https://doi.org/10.1016/j.oceaneng.2019.106462
  • S. Khan, E. Gunpinar, K. Mert Dogan, A novel design framework for gener- ation and parametric modification of yacht hull surfaces, Ocean Engineer- ing 136 (2017) 243–259. DOI: 10.1016/j.oceaneng.2017.03.013
  • J. J. L. Kitchley, A. Srivathsan, Generative methods and the design process: A design tool for conceptual settlement planning, Applied Soft Computing 14 (2014) 634–652. DOI:10.1016/j.asoc.2013.08.017
  • L. Caldas, Generation of energy-efficient architecture solutions applying gene arch: An evolution-based generative design system, Advanced Engi- neering Informatics 22 (1) (2008) 59–70. DOI: 10.1016/j.aei.2007.08.012
  • S. Khan, M. J. Awan, A generative design technique for exploring shape variations, Advanced Engineering Informatics 38 (2018) 712–724. https://doi.org/10.1016/j.aei.2018.10.005
  • Jiang, X., Lyu, R., Fukushima, Y., Otake, M., & Ju, D. Y., 2018: Lightweight Design and Analysis of Automobile Wheel Based on Bending and Radial Loads. IOP Conference Series: Materials Science and Engineering,372(1). https://doi.org/10.1088/1757899X/372/1/012048
  • Dede, G., Yıldızhan, Ş., Ökten, K., Çalık, A., Uludamar, E., & Özcanlı, M., 2017: Investigation of Stress and Displacement Distribution in Advanced Steel Rims. International Journal of Automotive Engineering and Technologies, 34–37. https://dergipark.org.tr/tr/pub/ijaet/issue/37926/438140
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Yusuf Burak Özdemir 0000-0002-2803-5702

Yalçın Karpuzcu 0000-0001-5784-3015

Serhat Çam 0000-0002-2174-3132

Erkan Günpınar 0000-0002-0266-5546

Yayımlanma Tarihi 19 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 24 Sayı: 72

Kaynak Göster

APA Özdemir, Y. B., Karpuzcu, Y., Çam, S., Günpınar, E. (2022). Wheel Rim Design Parametrization and Its Effect on Optimization. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 24(72), 913-926. https://doi.org/10.21205/deufmd.2022247220
AMA Özdemir YB, Karpuzcu Y, Çam S, Günpınar E. Wheel Rim Design Parametrization and Its Effect on Optimization. DEUFMD. Eylül 2022;24(72):913-926. doi:10.21205/deufmd.2022247220
Chicago Özdemir, Yusuf Burak, Yalçın Karpuzcu, Serhat Çam, ve Erkan Günpınar. “Wheel Rim Design Parametrization and Its Effect on Optimization”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 24, sy. 72 (Eylül 2022): 913-26. https://doi.org/10.21205/deufmd.2022247220.
EndNote Özdemir YB, Karpuzcu Y, Çam S, Günpınar E (01 Eylül 2022) Wheel Rim Design Parametrization and Its Effect on Optimization. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 24 72 913–926.
IEEE Y. B. Özdemir, Y. Karpuzcu, S. Çam, ve E. Günpınar, “Wheel Rim Design Parametrization and Its Effect on Optimization”, DEUFMD, c. 24, sy. 72, ss. 913–926, 2022, doi: 10.21205/deufmd.2022247220.
ISNAD Özdemir, Yusuf Burak vd. “Wheel Rim Design Parametrization and Its Effect on Optimization”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 24/72 (Eylül 2022), 913-926. https://doi.org/10.21205/deufmd.2022247220.
JAMA Özdemir YB, Karpuzcu Y, Çam S, Günpınar E. Wheel Rim Design Parametrization and Its Effect on Optimization. DEUFMD. 2022;24:913–926.
MLA Özdemir, Yusuf Burak vd. “Wheel Rim Design Parametrization and Its Effect on Optimization”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, c. 24, sy. 72, 2022, ss. 913-26, doi:10.21205/deufmd.2022247220.
Vancouver Özdemir YB, Karpuzcu Y, Çam S, Günpınar E. Wheel Rim Design Parametrization and Its Effect on Optimization. DEUFMD. 2022;24(72):913-26.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.