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Barlat89 Akma Kriterinin Detaylı İncelenmesi ve Model Katsayılarının Belirlenmesi Üzerine Farklı Yöntemlerin Geliştirilmesi

Yıl 2024, Cilt: 10 Sayı: 2, 233 - 245, 31.12.2024
https://doi.org/10.34186/klujes.1565385

Öz

Bu çalışmada, Barlat89 akma kriteri kullanılarak farklı anizotropik malzemelerin deformasyon davranışlarının modellenmesi üzerine odaklanılmıştır. Söz konusu kriter, ortotropik levhaların plastik davranışlarını öngörmek amacıyla geliştirilmiş bir modeldir. Sonlu elemanlar analizlerinde yaygın bir şekilde kullanılan Hill48 modeline kıyasla, daha az sayıda parametre ile yüksek doğruluk sağlaması nedeniyle tercih edilmektedir. Çalışmada, AA5754 ve AA7075 alüminyum alaşımları ile DP600 çift fazlı çelik malzemeler analiz edilmiştir. Model parametrelerinin belirlenmesinde, sayısal ve analitik yöntemler birlikte kullanılmış ve yeni parametrik uyarlama yöntemleri önerilmiştir. Elde edilen model sonuçları deneysel verilerle karşılaştırılmıştır. Bulgular, kullanılan çözüm yöntemlerin deformasyon davranışlarının öngörülmesinde önemli bir rol oynadığını ortaya koymuştur. Ayrıca, modelin doğruluğunu artırmak için parametrik uyarlama yöntemlerinin etkili bir araç olduğu tespit edilmiştir. Çalışmada, yalnızca bir parametrenin veya tüm model parametrelerinin optimize edilmesinin tahmin doğruluğu üzerindeki etkisi değerlendirilmiştir. Bu bağlamda, önerilen yöntemlerin farklı malzeme türleri ve gerilme koşulları için geçerliliği detaylı bir şekilde incelenmiştir.

Kaynakça

  • Aydın, M. S., Gerlach, J., Kessler, L., & Tekkaya, A. E. (2011). Yield locus evolution and constitutive parameter identification using plane strain tension and tensile tests. Journal of Materials Processing Technology, 211(12), 1957-1964. doi:https://doi.org/10.1016/j.jmatprotec.2011.06.018
  • Banabic, D. (2010). Sheet metal forming processes: constitutive modelling and numerical simulation: Springer Science & Business Media.
  • Barlat, F., & Lian, K. (1989). Plastic behavior and stretchability of sheet metals. Part I: A yield function for orthotropic sheets under plane stress conditions. International Journal of Plasticity, 5(1), 51-66.
  • Chaparro, B. M., Thuillier, S., Menezes, L. F., Manach, P. Y., & Fernandes, J. V. (2008). Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms. Computational Materials Science, 44(2), 339-346. doi:https://doi.org/10.1016/j.commatsci.2008.03.028
  • Conde, M., Coppieters, S., & Andrade-Campos, A. (2024). Strategies for automatic constitutive model selection and recommendation. International Journal of Mechanical Sciences, 264, 108813.
  • Dang, G. L., Đức, H. T., Lê Quốc, D., Van Chinh, N., & Van Hoang, N. (2023). Dự đoán sự tạo thành tai khi dập vuốt chi tiết hình trụ từ thép SUS304 bằng mô phỏng số. Journal of Military Science and Technology, 86, 129-136.
  • David, E. G. (1989). Genetic algorithms in search. Optimization and Machine Learning, Reading, Massachusetts. de Carvalho, M. A. C. F. (2023). Industrialization of a dual-phase steel stamped component: Process optimization using AutoForm software.
  • Du, K., Huang, S., Wang, H., Yu, F., Pan, L., Huang, H., . . . Yuan, X. (2022). Effect of different yield criteria and material parameter identification methods on the description accuracy of the anisotropic behavior of 5182-O aluminum alloy. Journal of materials engineering and performance, 1-19.
  • Erice, B., Rolfe, B., & Mendiguren, J. (2023). Anisotropic plasticity and fracture modelling of cold rolled AA5754. Engineering fracture mechanics, 289, 109471. doi:https://doi.org/10.1016/j.engfracmech.2023.109471
  • Goldberg, D. E., & Holland, J. H. (1988). Genetic Algorithms and Machine Learning. Machine learning, 3(2), 95-99. doi:10.1023/A:1022602019183
  • Hou, Y., Myung, D., Park, J. K., Min, J., Lee, H.-R., El-Aty, A. A., & Lee, M.-G. (2023). A Review of Characterization and Modelling Approaches for Sheet Metal Forming of Lightweight Metallic Materials. Materials, 16(2), 836. Retrieved from https://www.mdpi.com/1996-1944/16/2/836
  • Kacar, I., Öztürk, F., Toros, S., & Kılıç, S. (2020). Prediction of strain limits via the Marciniak-Kuczynski model and a novel semi-empirical forming limit diagram model for dual-phase DP600 advanced high strength steel.
  • Katiyar, B. S., Panicker, S. S., & Panda, S. K. (2023). Crushing Performance of AA5754 and AA6082 Shells Fabricated by Warm Redrawing Process. Journal of materials engineering and performance, 1-11.
  • Kilic, S. (2019). Experimental and numerical investigation of the effect of different temperature and deformation speeds on mechanical properties and springback behaviour in Al-Zn-Mg-Cu alloy. Mechanika, 25(5), 406-412. doi:10.5755/j01.mech.25.5.22689
  • Kilic, S., & Ozturk, F. (2016). Comparison of performances of commercial TWIP900 and DP600 advanced high strength steels in automotive industry.
  • Kılıç, S. (2024). Hill48 akma kriteri kullanarak alüminyum alaşımlarının anizotropik davranışlarının modellenmesi ve optimizasyonu. [Modeling and optimization of the anisotropic behavior of aluminum alloys by using the Hill48 yield criterion]. International Journal of Engineering Design and Technology, 6(1), 16-21. Retrieved from https://dergipark.org.tr/tr/pub/ijedt/issue/84209/1496394
  • Lagarias, J. C., Reeds, J. A., Wright, M. H., & Wright, P. E. (1998). Convergence properties of the Nelder--Mead simplex method in low dimensions. SIAM Journal on optimization, 9(1), 112-147.
  • Lei, C., Mao, J., Zhang, X., Liu, J., Wang, L., & Chen, D. (2021). A comparison study of the yield surface exponent of the Barlat yield function on the forming limit curve prediction of zirconium alloys with MK method. International Journal of Material Forming, 14, 467-484.
  • Ozturk, F., Pekel, H., & Halkaci, H. S. (2011). The Effect of Strain-Rate Sensitivity on Formability of AA 5754-O at Cold and Warm Temperatures. Journal of materials engineering and performance, 20(1), 77-81. doi:10.1007/s11665-010-9652-y
  • Rajendran, P., Duraisamy, V., Rajendran, A. R., & Loganathan, R. V. (2023). Optimization on the electrical discharge machining (EDM) process parameters of aged AA7075/TiC metal matrix composites. Revista De Metalurgia, 59(3), e245-e245.
  • Rickhey, F., & Hong, S. (2023). Validation of axial and transverse force–displacement responses and principal strain rate ratios in the critical zone as a precursor to anisotropic damage prediction in metal sheets. International Journal of Material Forming, 16(1), 10.
  • Sanrutsadakorn, A., Jhonthong, N., & Julsri, W. (2023). Finite element modeling for analyzing the production of high-strength steel sheets for automobile parts.
  • Saxena, K. K., Drotleff, K., & Mukhopadhyay, J. (2016). Elevated temperature forming limit strain diagrams of automotive alloys Al6014-T4 and DP600: A case study. The Journal of Strain Analysis for Engineering Design, 51(6), 459-470.
  • Shrivastava, A., & Digavalli, R. K. (2023). Effect of Process Variables on Interface Friction Characteristics in Strip Drawing of AA 5182 Alloy and Its Formability in Warm Deep Drawing. Journal of Manufacturing and Materials Processing, 7(5), 175.
  • Shrivastava, A., & Kumar, D. R. (2024). Deep drawing simulation of dual phase steel using hardening curves and anisotropic parameters from uniaxial and biaxial tensile tests. Paper presented at the IOP Conference Series: Materials Science and Engineering.
  • Toros, S., Alkan, M., Ece, R. E., & Ozturk, F. (2011). Effect of pre-straining on the springback behavior of the AA5754-0 alloy. Materiali in Tehnologije, 45(6), 613-618.
  • Yang, H., Chen, J., Hong, Q., & Chen, W. (2023). Development of combined hardening model for spring-back simulation of DP600 in multi-stage sheet metal forming. Paper presented at the Journal of Physics: Conference Series.

Detailed Examination of the Barlat89 Yield Criterion and Development of Various Methods for Determining Model Coefficients

Yıl 2024, Cilt: 10 Sayı: 2, 233 - 245, 31.12.2024
https://doi.org/10.34186/klujes.1565385

Öz

In this study, the modeling of deformation behavior of various anisotropic materials using the Barlat89 yield criterion has been focused on. This criterion is a model developed to predict the plastic behavior of orthotropic sheets. Compared to the Hill48 model, which is widely used in finite element analyses, it is preferred due to its higher accuracy with fewer parameters. In the study, AA5754 and AA7075 aluminum alloys and DP600 dual-phase steel materials were analyzed. In determining the model parameters, numerical and analytical methods were used together, and new parametric adaptation methods were proposed. The obtained model results were compared with experimental data. The findings revealed that the solution methods used play a significant role in predicting deformation behavior. Additionally, it has been determined that parametric adaptation methods are an effective tool for improving the accuracy of the model. In the study, the effect of optimizing only one parameter or all model parameters on prediction accuracy was evaluated. In this context, the validity of the proposed methods for different material types and stress conditions was investigated in detail.

Kaynakça

  • Aydın, M. S., Gerlach, J., Kessler, L., & Tekkaya, A. E. (2011). Yield locus evolution and constitutive parameter identification using plane strain tension and tensile tests. Journal of Materials Processing Technology, 211(12), 1957-1964. doi:https://doi.org/10.1016/j.jmatprotec.2011.06.018
  • Banabic, D. (2010). Sheet metal forming processes: constitutive modelling and numerical simulation: Springer Science & Business Media.
  • Barlat, F., & Lian, K. (1989). Plastic behavior and stretchability of sheet metals. Part I: A yield function for orthotropic sheets under plane stress conditions. International Journal of Plasticity, 5(1), 51-66.
  • Chaparro, B. M., Thuillier, S., Menezes, L. F., Manach, P. Y., & Fernandes, J. V. (2008). Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms. Computational Materials Science, 44(2), 339-346. doi:https://doi.org/10.1016/j.commatsci.2008.03.028
  • Conde, M., Coppieters, S., & Andrade-Campos, A. (2024). Strategies for automatic constitutive model selection and recommendation. International Journal of Mechanical Sciences, 264, 108813.
  • Dang, G. L., Đức, H. T., Lê Quốc, D., Van Chinh, N., & Van Hoang, N. (2023). Dự đoán sự tạo thành tai khi dập vuốt chi tiết hình trụ từ thép SUS304 bằng mô phỏng số. Journal of Military Science and Technology, 86, 129-136.
  • David, E. G. (1989). Genetic algorithms in search. Optimization and Machine Learning, Reading, Massachusetts. de Carvalho, M. A. C. F. (2023). Industrialization of a dual-phase steel stamped component: Process optimization using AutoForm software.
  • Du, K., Huang, S., Wang, H., Yu, F., Pan, L., Huang, H., . . . Yuan, X. (2022). Effect of different yield criteria and material parameter identification methods on the description accuracy of the anisotropic behavior of 5182-O aluminum alloy. Journal of materials engineering and performance, 1-19.
  • Erice, B., Rolfe, B., & Mendiguren, J. (2023). Anisotropic plasticity and fracture modelling of cold rolled AA5754. Engineering fracture mechanics, 289, 109471. doi:https://doi.org/10.1016/j.engfracmech.2023.109471
  • Goldberg, D. E., & Holland, J. H. (1988). Genetic Algorithms and Machine Learning. Machine learning, 3(2), 95-99. doi:10.1023/A:1022602019183
  • Hou, Y., Myung, D., Park, J. K., Min, J., Lee, H.-R., El-Aty, A. A., & Lee, M.-G. (2023). A Review of Characterization and Modelling Approaches for Sheet Metal Forming of Lightweight Metallic Materials. Materials, 16(2), 836. Retrieved from https://www.mdpi.com/1996-1944/16/2/836
  • Kacar, I., Öztürk, F., Toros, S., & Kılıç, S. (2020). Prediction of strain limits via the Marciniak-Kuczynski model and a novel semi-empirical forming limit diagram model for dual-phase DP600 advanced high strength steel.
  • Katiyar, B. S., Panicker, S. S., & Panda, S. K. (2023). Crushing Performance of AA5754 and AA6082 Shells Fabricated by Warm Redrawing Process. Journal of materials engineering and performance, 1-11.
  • Kilic, S. (2019). Experimental and numerical investigation of the effect of different temperature and deformation speeds on mechanical properties and springback behaviour in Al-Zn-Mg-Cu alloy. Mechanika, 25(5), 406-412. doi:10.5755/j01.mech.25.5.22689
  • Kilic, S., & Ozturk, F. (2016). Comparison of performances of commercial TWIP900 and DP600 advanced high strength steels in automotive industry.
  • Kılıç, S. (2024). Hill48 akma kriteri kullanarak alüminyum alaşımlarının anizotropik davranışlarının modellenmesi ve optimizasyonu. [Modeling and optimization of the anisotropic behavior of aluminum alloys by using the Hill48 yield criterion]. International Journal of Engineering Design and Technology, 6(1), 16-21. Retrieved from https://dergipark.org.tr/tr/pub/ijedt/issue/84209/1496394
  • Lagarias, J. C., Reeds, J. A., Wright, M. H., & Wright, P. E. (1998). Convergence properties of the Nelder--Mead simplex method in low dimensions. SIAM Journal on optimization, 9(1), 112-147.
  • Lei, C., Mao, J., Zhang, X., Liu, J., Wang, L., & Chen, D. (2021). A comparison study of the yield surface exponent of the Barlat yield function on the forming limit curve prediction of zirconium alloys with MK method. International Journal of Material Forming, 14, 467-484.
  • Ozturk, F., Pekel, H., & Halkaci, H. S. (2011). The Effect of Strain-Rate Sensitivity on Formability of AA 5754-O at Cold and Warm Temperatures. Journal of materials engineering and performance, 20(1), 77-81. doi:10.1007/s11665-010-9652-y
  • Rajendran, P., Duraisamy, V., Rajendran, A. R., & Loganathan, R. V. (2023). Optimization on the electrical discharge machining (EDM) process parameters of aged AA7075/TiC metal matrix composites. Revista De Metalurgia, 59(3), e245-e245.
  • Rickhey, F., & Hong, S. (2023). Validation of axial and transverse force–displacement responses and principal strain rate ratios in the critical zone as a precursor to anisotropic damage prediction in metal sheets. International Journal of Material Forming, 16(1), 10.
  • Sanrutsadakorn, A., Jhonthong, N., & Julsri, W. (2023). Finite element modeling for analyzing the production of high-strength steel sheets for automobile parts.
  • Saxena, K. K., Drotleff, K., & Mukhopadhyay, J. (2016). Elevated temperature forming limit strain diagrams of automotive alloys Al6014-T4 and DP600: A case study. The Journal of Strain Analysis for Engineering Design, 51(6), 459-470.
  • Shrivastava, A., & Digavalli, R. K. (2023). Effect of Process Variables on Interface Friction Characteristics in Strip Drawing of AA 5182 Alloy and Its Formability in Warm Deep Drawing. Journal of Manufacturing and Materials Processing, 7(5), 175.
  • Shrivastava, A., & Kumar, D. R. (2024). Deep drawing simulation of dual phase steel using hardening curves and anisotropic parameters from uniaxial and biaxial tensile tests. Paper presented at the IOP Conference Series: Materials Science and Engineering.
  • Toros, S., Alkan, M., Ece, R. E., & Ozturk, F. (2011). Effect of pre-straining on the springback behavior of the AA5754-0 alloy. Materiali in Tehnologije, 45(6), 613-618.
  • Yang, H., Chen, J., Hong, Q., & Chen, W. (2023). Development of combined hardening model for spring-back simulation of DP600 in multi-stage sheet metal forming. Paper presented at the Journal of Physics: Conference Series.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Makine Mühendisliğinde Optimizasyon Teknikleri, Malzeme Tasarım ve Davranışları
Bölüm Makaleler
Yazarlar

Süleyman Kılıç 0000-0002-1681-9403

Erken Görünüm Tarihi 25 Aralık 2024
Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 11 Ekim 2024
Kabul Tarihi 10 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 10 Sayı: 2

Kaynak Göster

APA Kılıç, S. (2024). Barlat89 Akma Kriterinin Detaylı İncelenmesi ve Model Katsayılarının Belirlenmesi Üzerine Farklı Yöntemlerin Geliştirilmesi. Kirklareli University Journal of Engineering and Science, 10(2), 233-245. https://doi.org/10.34186/klujes.1565385