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GMDH sinir ağı kullanılarak elastomer tabakalar üzerinde küresel elastomerik yatağın maksimum gerilmesinin tahmini

Year 2021, Volume: 36 Issue: 3, 1331 - 1346, 24.05.2021
https://doi.org/10.17341/gazimmfd.722514

Abstract

Uluslararası literatürde düzlemsel elastomerik yataklarla ilgili birçok çalışma dikkati çekerken, çok katmanlı olmasından ve tasarım zorluklarından dolayı küresel elastomerik yataklarla ilgili çok az çalışma bulunmaktadır. Elastomerik yataklar, tabakalara dik gelen yüklere karşı rijitken tabakalara paralel gelen yüklere karşı esnektir. Böylece küresel elastomerik yataklar helikopter pervanelerinin dönmesinden kaynaklı merkez kaç kuvvetine karşı rijit, pervanenin kanat çırpma ve dönme hareketine karşı esneklik sağlamaktadır. Elastomer malzeme üzerindeki gerilmeler, yatağın ömrünü azaltır; bu ise, maksimum gerilmenin azaltılmasının, elastomerik yatak ömrü için çok önemli olduğunu gösterir. Bu çalışmada elastomer tabakalar üzerindeki maksimum gerilme, basınç yüklemesi ve açısal yer değiştirme yüklemesine maruz küresel elastomerik yatağın, delik çapının, delik şeklinin, elastomer tabaka kalınlığının, tabaka sayısının ve elastomer yatak profili ile tahmin etmek için GMDH modeli kullanılmıştır. GMDH modeline giriş olarak θ(açısal yer değiştirme yüklemesi), P (basınç yüklemesi), a(eksen yarıçapı), β0 (birinci joint açısı), cos(β0) (birinci joint açı cosinüs değeri), β1 (ikinci joint açısı), β2(üçüncü joint açısı), φt(koni açısı), φp(basınç yüklemesinin doğrultusuyla elastomer tabakaya dik düzlem arasındaki açı), cos(φp), D(elastomer tabaka dış çapı), ne (elastomer tabaka sayısı), d(elastomer tabaka delik çapı) ve H elastomer tabaka kalınlığı) değişkenleri kullanılmıştır. GMDH ile elde edilen sonuçlar ANN, SVM, RF gibi farklı makine öğrenmesi yöntemler ile de karşılaştırılmıştır. Elde edilen sonuçlara göre GMDH modeli maksimum gerilmeleri tahmin etmede diğer modellere göre daha başarılı bulunmuştur.

References

  • [1] Kuncan, M., Kaplan, K., Mi̇naz, M. R., Kaya, Y., & Ertunç, H. M. (2019). A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. ISA transactions.
  • [2] Kaya, Y., Kuncan, M., Kaplan, K., Minaz, M. R., & Ertunç, H. M. (2020). Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters. Soft Computing, 1-12.
  • [3] Kaplan, K., Kaya, Y., Kuncan, M., Mi̇naz, M. R., & Ertunç, H. M. (2020). An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Applied Soft Computing, 87, 106019.
  • [4] Kaya, Y., Kuncan, M., Kaplan, K., Minaz, M. R., & Ertunç, H. M. (2020). A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification. Journal of Experimental & Theoretical Artificial Intelligence, 1-18.
  • [5] Panda, B., Mychalowycz, E., & Tarzanin, F. J., Application of Passive Dampers to Modern Helicopters. Smart Materials and Structures, 1996, 5(5), 509.
  • [6] Roeder, C. W., Stanton, J. F., & Taylor, A. W., Fatigue of Steel-Reinforced Elastomeric Bearings. Journal of Structural Engineering, 1990, 116(2), 407-426.
  • [7] Naghshineh A. K., Experimental Studies on Fiber-Mesh Reinforced Elastomeric Bearings, PhD Thesis, Middle East Technical University, Civil Engineering, Ankara, 2013.
  • [8] Domaniç K. A., Seismic Performance of Unbonded Elastomeric Bearings on Bridges: An Experimental and Parametric Study, PhD Thesis, Middle East Technical University, Civil Engineering, Ankara, 2015.
  • [9] Ruano P. C., Strauss A., An Experimental Study on Unbonded Circular Fiber Reinforced Elastomeric Bearings, Eng. Struct., 2018, 177, 72-84.
  • [10] Chen G.-S., Zhang L. H., Li F.P., Qin H.Y., Li M. F., Finite Element Analysis for the Influence of Spherical Layered Elastomeric Bearing Structure on the Mechanical Behavior, Cailiao Gongcheng/Journal Mater. Eng., 2009, 10, 005.
  • [11] Su H., Ren J. X., Xue M. Y., Tong Y., Zheng Q., Yang J. X., Influence of Pressure and Deflection Loads on the Critical Behavior of Flexible Joints, Compos. Struct., 2017, 180, 772-781.
  • [12] Ren J., Zhang X., Yang J., Wang C., Liu Y., Yang W., Structural Analysis and Testing of a Miniature Flexible Joint under Pressure and Vector Loading, J. Mech. Sci. Technol., 2014, 28(9), 3637-3643.
  • [13] Bayraklılar M. S., Helikopterlerde Kullanılan Küresel Elastomerik Yatakların Mekanik Tasarımı, PhD Thesis, Kocaeli University, Kocaeli, 2019.
  • [14] Ogden R., Large Deformation Isotropic Elasticity-on the Correlation of Theory and Experiment for Incompressible Rubberlike Solids, Proc. R. Soc. London. Ser. A, 1972, 326(1567), 565-584.
  • [15] Mooney M., A Theory of Large Elastic Deformation, J. Appl. Phys., 1940, 11(9), 582-592.
  • [16] Shahzad M., Kamran A., Siddiqui M. Z., Farhan M., Mechanical Characterization and FE Modelling of a Hyperelastic Material, Mater. Res., 2015, 18(5), 918-924.
  • [17] Dere, M., & Filiz, İ. H. (2018). Otomat çeliğinin tornalama işleminde iş parçası çapı ve çıkıntı uzunluğunun yüzey pürüzlülüğü üzerindeki etkilerinin deneysel incelenmesi ve yüzey pürüzlülüğünün ANFIS ile modellenmesi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 34(2), 675-686.
  • [18] Küçük, H., Eminoğlu, İ., & Balcı, K. (2019). Nöromüsküler hastalıkların yapay zeka yöntemleri ile sınıflandırılması. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 34(4), 1725-1742.
  • [19] Reşat, H. G. (2020). Sürdürülebilir enerji yönetimi için yapay sinir ağları ve ARIMA metotları kullanılarak melez tahmin modelinin tasarlanması ve geliştirilmesi: Tütün endüstrisinde vaka çalışması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(3), 1129-1140.
  • [20] Kaplan, K., Kuncan, M., & Ertunc, H. M. (2015). Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system. IEEE 23nd Signal Processing and Communications Applications Conference (SIU 2015), Malatya, Turkey (pp. 1925–1928).
  • [21] Kaplan, K., Bayram, S., Kuncan, M., & Ertunç, H. M. (2014). Feature extraction of ball bearings in time-space and estimation of fault size with method of ANN. Proceedings of the 16th Mechatronika, Brno, Czech Republic.
  • [22] Nguyen T. N., Lee S., Nguyen-Xuan H., Lee J., A Novel Analysis-Prediction Approach for Geometrically Nonlinear Problems Using Group Method of Data Handling. Computer Methods in Applied Mechanics and Engineering, 2019, 354, 506-526.
  • [23] Jiang Y., Liu S., Peng L., Zhao N., A Novel Wind Speed Prediction Method Based on Robust Local Mean Decomposition, Group Method of Data Handling and Conditional Kernel Density Estimation, Energy Conversion and Management, 2019, 200, 112099.
  • [24] Naeini S. A., Moayed R. Z., Kordnaeij A., Mola-Abasi H., Elasticity Modulus of Clayey Deposits Estimation Using Group Method of Data Handling Type Neural Network. Measurement, 2018, 121, 335-343.
  • [25] Liu H., Duan Z., Wu H., Li Y., Dong S., Wind Speed Forecasting Models Based on Data Decomposition, Feature Selection and Group Method of Data Handling Network. Measurement, 2019, 148, 106971.
  • [26] No Y. G., Lee C., Seong P. H. Development of a Prediction Method for SAMG Entry Time in NPPs Using the Extended Group Method of Data Handling (GMDH) model, Annals of Nuclear Energy, 2018, 121, 552-566.
  • [27] Wu J., Wang Y., Zhang X., Chen Z., A Novel State of Health Estimation Method of Li-ion Battery Using Group Method of Data Handling. Journal of Power Sources, 2016, 327, 457-464.
  • [28] Kurnaz T. F., Kaya, Y., Prediction of the California Bearing Ratio (CBR) of Compacted Soils by Using GMDH-Type Neural Network. The European Physical Journal Plus, 2019, 134(7), 326.
  • [29] Khan A., Ko D. K., Lim S. C., Kim H. S., Structural vibration-based classification and prediction of delamination in smart composite laminates using deep learning neural network, Composites Part B: Engineering, 2019, 161, 586-594.
  • [30] Yang Z., Fu J., Bai J., Liao G., Yu M., An Inverse Model of Magnetorheological Elastomer Isolator with Neural Network, 2019 Chinese Control And Decision Conference (CCDC), (pp. 1664-1667), IEEE, June 2019.
  • [31] Vatandoost H., Alehashem S. M. S., Norouzi M., Taghavifar H., Ni Y. Q., A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode, IEEE Transactions on Magnetics, 2019, 55(12), 1-8.
  • [32] Yu Y., Li Y., Li J., Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm, Journal of Intelligent Material Systems and Structures, 2015, 26(14), 1789-1798.
  • [33] Nguyen X. B., Komatsuzaki T., Iwata Y., Asanuma H., Modeling and semi-active fuzzy control of magnetorheological elastomer-based isolator for seismic response reduction, Mechanical Systems and Signal Processing, 2018, 101, 449-466.
  • [34] Zhao S., Ma Y., Leng D., An Intelligent Artificial Neural Network Modeling of a Magnetorheological Elastomer Isolator, Algorithms, 2019, 12(9), 195.
  • [35] Leng D., Xu K., Ma Y., Liu G., Sun L., Modeling the behaviors of magnetorheological elastomer isolator in shear-compression mixed mode utilizing artificial neural network optimized by fuzzy algorithm (ANNOFA), Smart Materials and Structures, 2018, 27(11), 115026.
  • [36] Vatandoost H., Norouzi M., Alehashem S. M. S., Smoukov S. K., A novel phenomenological model for dynamic behavior of magnetorheological elastomers in tension–compression mode, Smart Materials and Structures, 2017, 26(6), 065011.
  • [37] Gu X., Yu Y., Li J., Li Y., Semi-active control of magnetorheological elastomer base isolation system utilising learning-based inverse model, Journal of Sound and Vibration, 2017, 406, 346-362.
Year 2021, Volume: 36 Issue: 3, 1331 - 1346, 24.05.2021
https://doi.org/10.17341/gazimmfd.722514

Abstract

References

  • [1] Kuncan, M., Kaplan, K., Mi̇naz, M. R., Kaya, Y., & Ertunç, H. M. (2019). A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. ISA transactions.
  • [2] Kaya, Y., Kuncan, M., Kaplan, K., Minaz, M. R., & Ertunç, H. M. (2020). Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters. Soft Computing, 1-12.
  • [3] Kaplan, K., Kaya, Y., Kuncan, M., Mi̇naz, M. R., & Ertunç, H. M. (2020). An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Applied Soft Computing, 87, 106019.
  • [4] Kaya, Y., Kuncan, M., Kaplan, K., Minaz, M. R., & Ertunç, H. M. (2020). A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification. Journal of Experimental & Theoretical Artificial Intelligence, 1-18.
  • [5] Panda, B., Mychalowycz, E., & Tarzanin, F. J., Application of Passive Dampers to Modern Helicopters. Smart Materials and Structures, 1996, 5(5), 509.
  • [6] Roeder, C. W., Stanton, J. F., & Taylor, A. W., Fatigue of Steel-Reinforced Elastomeric Bearings. Journal of Structural Engineering, 1990, 116(2), 407-426.
  • [7] Naghshineh A. K., Experimental Studies on Fiber-Mesh Reinforced Elastomeric Bearings, PhD Thesis, Middle East Technical University, Civil Engineering, Ankara, 2013.
  • [8] Domaniç K. A., Seismic Performance of Unbonded Elastomeric Bearings on Bridges: An Experimental and Parametric Study, PhD Thesis, Middle East Technical University, Civil Engineering, Ankara, 2015.
  • [9] Ruano P. C., Strauss A., An Experimental Study on Unbonded Circular Fiber Reinforced Elastomeric Bearings, Eng. Struct., 2018, 177, 72-84.
  • [10] Chen G.-S., Zhang L. H., Li F.P., Qin H.Y., Li M. F., Finite Element Analysis for the Influence of Spherical Layered Elastomeric Bearing Structure on the Mechanical Behavior, Cailiao Gongcheng/Journal Mater. Eng., 2009, 10, 005.
  • [11] Su H., Ren J. X., Xue M. Y., Tong Y., Zheng Q., Yang J. X., Influence of Pressure and Deflection Loads on the Critical Behavior of Flexible Joints, Compos. Struct., 2017, 180, 772-781.
  • [12] Ren J., Zhang X., Yang J., Wang C., Liu Y., Yang W., Structural Analysis and Testing of a Miniature Flexible Joint under Pressure and Vector Loading, J. Mech. Sci. Technol., 2014, 28(9), 3637-3643.
  • [13] Bayraklılar M. S., Helikopterlerde Kullanılan Küresel Elastomerik Yatakların Mekanik Tasarımı, PhD Thesis, Kocaeli University, Kocaeli, 2019.
  • [14] Ogden R., Large Deformation Isotropic Elasticity-on the Correlation of Theory and Experiment for Incompressible Rubberlike Solids, Proc. R. Soc. London. Ser. A, 1972, 326(1567), 565-584.
  • [15] Mooney M., A Theory of Large Elastic Deformation, J. Appl. Phys., 1940, 11(9), 582-592.
  • [16] Shahzad M., Kamran A., Siddiqui M. Z., Farhan M., Mechanical Characterization and FE Modelling of a Hyperelastic Material, Mater. Res., 2015, 18(5), 918-924.
  • [17] Dere, M., & Filiz, İ. H. (2018). Otomat çeliğinin tornalama işleminde iş parçası çapı ve çıkıntı uzunluğunun yüzey pürüzlülüğü üzerindeki etkilerinin deneysel incelenmesi ve yüzey pürüzlülüğünün ANFIS ile modellenmesi. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 34(2), 675-686.
  • [18] Küçük, H., Eminoğlu, İ., & Balcı, K. (2019). Nöromüsküler hastalıkların yapay zeka yöntemleri ile sınıflandırılması. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 34(4), 1725-1742.
  • [19] Reşat, H. G. (2020). Sürdürülebilir enerji yönetimi için yapay sinir ağları ve ARIMA metotları kullanılarak melez tahmin modelinin tasarlanması ve geliştirilmesi: Tütün endüstrisinde vaka çalışması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 35(3), 1129-1140.
  • [20] Kaplan, K., Kuncan, M., & Ertunc, H. M. (2015). Prediction of bearing fault size by using model of adaptive neuro-fuzzy inference system. IEEE 23nd Signal Processing and Communications Applications Conference (SIU 2015), Malatya, Turkey (pp. 1925–1928).
  • [21] Kaplan, K., Bayram, S., Kuncan, M., & Ertunç, H. M. (2014). Feature extraction of ball bearings in time-space and estimation of fault size with method of ANN. Proceedings of the 16th Mechatronika, Brno, Czech Republic.
  • [22] Nguyen T. N., Lee S., Nguyen-Xuan H., Lee J., A Novel Analysis-Prediction Approach for Geometrically Nonlinear Problems Using Group Method of Data Handling. Computer Methods in Applied Mechanics and Engineering, 2019, 354, 506-526.
  • [23] Jiang Y., Liu S., Peng L., Zhao N., A Novel Wind Speed Prediction Method Based on Robust Local Mean Decomposition, Group Method of Data Handling and Conditional Kernel Density Estimation, Energy Conversion and Management, 2019, 200, 112099.
  • [24] Naeini S. A., Moayed R. Z., Kordnaeij A., Mola-Abasi H., Elasticity Modulus of Clayey Deposits Estimation Using Group Method of Data Handling Type Neural Network. Measurement, 2018, 121, 335-343.
  • [25] Liu H., Duan Z., Wu H., Li Y., Dong S., Wind Speed Forecasting Models Based on Data Decomposition, Feature Selection and Group Method of Data Handling Network. Measurement, 2019, 148, 106971.
  • [26] No Y. G., Lee C., Seong P. H. Development of a Prediction Method for SAMG Entry Time in NPPs Using the Extended Group Method of Data Handling (GMDH) model, Annals of Nuclear Energy, 2018, 121, 552-566.
  • [27] Wu J., Wang Y., Zhang X., Chen Z., A Novel State of Health Estimation Method of Li-ion Battery Using Group Method of Data Handling. Journal of Power Sources, 2016, 327, 457-464.
  • [28] Kurnaz T. F., Kaya, Y., Prediction of the California Bearing Ratio (CBR) of Compacted Soils by Using GMDH-Type Neural Network. The European Physical Journal Plus, 2019, 134(7), 326.
  • [29] Khan A., Ko D. K., Lim S. C., Kim H. S., Structural vibration-based classification and prediction of delamination in smart composite laminates using deep learning neural network, Composites Part B: Engineering, 2019, 161, 586-594.
  • [30] Yang Z., Fu J., Bai J., Liao G., Yu M., An Inverse Model of Magnetorheological Elastomer Isolator with Neural Network, 2019 Chinese Control And Decision Conference (CCDC), (pp. 1664-1667), IEEE, June 2019.
  • [31] Vatandoost H., Alehashem S. M. S., Norouzi M., Taghavifar H., Ni Y. Q., A Supervised Artificial Neural Network-Assisted Modeling of Magnetorheological Elastomers in Tension–Compression Mode, IEEE Transactions on Magnetics, 2019, 55(12), 1-8.
  • [32] Yu Y., Li Y., Li J., Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm, Journal of Intelligent Material Systems and Structures, 2015, 26(14), 1789-1798.
  • [33] Nguyen X. B., Komatsuzaki T., Iwata Y., Asanuma H., Modeling and semi-active fuzzy control of magnetorheological elastomer-based isolator for seismic response reduction, Mechanical Systems and Signal Processing, 2018, 101, 449-466.
  • [34] Zhao S., Ma Y., Leng D., An Intelligent Artificial Neural Network Modeling of a Magnetorheological Elastomer Isolator, Algorithms, 2019, 12(9), 195.
  • [35] Leng D., Xu K., Ma Y., Liu G., Sun L., Modeling the behaviors of magnetorheological elastomer isolator in shear-compression mixed mode utilizing artificial neural network optimized by fuzzy algorithm (ANNOFA), Smart Materials and Structures, 2018, 27(11), 115026.
  • [36] Vatandoost H., Norouzi M., Alehashem S. M. S., Smoukov S. K., A novel phenomenological model for dynamic behavior of magnetorheological elastomers in tension–compression mode, Smart Materials and Structures, 2017, 26(6), 065011.
  • [37] Gu X., Yu Y., Li J., Li Y., Semi-active control of magnetorheological elastomer base isolation system utilising learning-based inverse model, Journal of Sound and Vibration, 2017, 406, 346-362.
There are 37 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Yılmaz Kaya 0000-0001-5167-1101

Murat Makaracı This is me 0000-0002-7952-1989

Said Bayraklılar 0000-0002-5365-4441

Melih Kuncan 0000-0002-9749-0418

Publication Date May 24, 2021
Submission Date April 21, 2020
Acceptance Date January 25, 2021
Published in Issue Year 2021 Volume: 36 Issue: 3

Cite

APA Kaya, Y., Makaracı, M., Bayraklılar, S., Kuncan, M. (2021). GMDH sinir ağı kullanılarak elastomer tabakalar üzerinde küresel elastomerik yatağın maksimum gerilmesinin tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(3), 1331-1346. https://doi.org/10.17341/gazimmfd.722514
AMA Kaya Y, Makaracı M, Bayraklılar S, Kuncan M. GMDH sinir ağı kullanılarak elastomer tabakalar üzerinde küresel elastomerik yatağın maksimum gerilmesinin tahmini. GUMMFD. May 2021;36(3):1331-1346. doi:10.17341/gazimmfd.722514
Chicago Kaya, Yılmaz, Murat Makaracı, Said Bayraklılar, and Melih Kuncan. “GMDH Sinir ağı kullanılarak Elastomer Tabakalar üzerinde küresel Elastomerik yatağın Maksimum Gerilmesinin Tahmini”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, no. 3 (May 2021): 1331-46. https://doi.org/10.17341/gazimmfd.722514.
EndNote Kaya Y, Makaracı M, Bayraklılar S, Kuncan M (May 1, 2021) GMDH sinir ağı kullanılarak elastomer tabakalar üzerinde küresel elastomerik yatağın maksimum gerilmesinin tahmini. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36 3 1331–1346.
IEEE Y. Kaya, M. Makaracı, S. Bayraklılar, and M. Kuncan, “GMDH sinir ağı kullanılarak elastomer tabakalar üzerinde küresel elastomerik yatağın maksimum gerilmesinin tahmini”, GUMMFD, vol. 36, no. 3, pp. 1331–1346, 2021, doi: 10.17341/gazimmfd.722514.
ISNAD Kaya, Yılmaz et al. “GMDH Sinir ağı kullanılarak Elastomer Tabakalar üzerinde küresel Elastomerik yatağın Maksimum Gerilmesinin Tahmini”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/3 (May 2021), 1331-1346. https://doi.org/10.17341/gazimmfd.722514.
JAMA Kaya Y, Makaracı M, Bayraklılar S, Kuncan M. GMDH sinir ağı kullanılarak elastomer tabakalar üzerinde küresel elastomerik yatağın maksimum gerilmesinin tahmini. GUMMFD. 2021;36:1331–1346.
MLA Kaya, Yılmaz et al. “GMDH Sinir ağı kullanılarak Elastomer Tabakalar üzerinde küresel Elastomerik yatağın Maksimum Gerilmesinin Tahmini”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 36, no. 3, 2021, pp. 1331-46, doi:10.17341/gazimmfd.722514.
Vancouver Kaya Y, Makaracı M, Bayraklılar S, Kuncan M. GMDH sinir ağı kullanılarak elastomer tabakalar üzerinde küresel elastomerik yatağın maksimum gerilmesinin tahmini. GUMMFD. 2021;36(3):1331-46.