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DP1200 Çeliği Lazer Kaynak İşleminde Proses Parametrelerinin Mekanik ve Geometrik Özelliklere Etkisinin Bulanık Mantık Yöntemi ile Tahmini ve Optimizasyonu

Year 2023, , 299 - 316, 30.04.2023
https://doi.org/10.53433/yyufbed.1195234

Abstract

Bu çalışmada DP 1200 çelik malzemelerin bindirme konfigürasyonunda fiber lazer kaynak yöntemi ile birleştirilmesi gerçekleştirilmiştir. Kaynaklı bağlantıların kaynak geometrisi ve mekanik özellikleri lazer gücü, ilerleme hızı ve lazer açısı proses parametrelerine göre değişkenlik göstermektedir. Parametrelerin etkilerini gözlemleyebilmek için Yanıt Yüzey Metodolojisine göre deney tasarımı oluşturularak deneyler gerçekleştirilmiştir. Çekme testi sonrasında kesme kuvveti değerleri elde edilmiştir; kaynak geometrisinde ise tam birleşme mesafesi ölçülmüştür. Deneysel çıktılar, kesme kuvveti ve birleşme mesafesini tahmin etmede Mamdani yöntemine göre geliştirilen Bulanık Mantık modelinde kullanılmıştır, 27 adet kural tanımlanmıştır. Tahmin sonuçları ve deneysel veriler kıyaslandığında birbiri ile benzerdir. Yüzey grafikleri yardımıyla optimum proses parametreleri lazer gücü 2800 W, ilerleme hızı 40 mm/s, lazer açısı 70ᵒ olarak belirlenmiştir.

Supporting Institution

Bursa Uludağ Üniversitesi Bilimsel Araştırmalar Proje Birimi

Project Number

OUAP (MH)-2019-6

Thanks

Bu çalışma Bursa Uludağ Üniversitesi Bilimsel Araştırmalar Proje Birimi tarafından desteklenen OUAP (MH)-2019-6 numaralı proje kapsamında gerçekleştirilmiştir. Yazarlar, fiber lazer kaynak işlemlerinin gerçekleştirilmesinde sağladığı imkanlar dolayısıyla LASER ISSE firmasına teşekkür eder. Not: Bu çalışmada, yazarı Meryem ALTAY olan “708282” numaralı “Otomotiv Endüstrisinde Kullanılan Yüksek Mukavemetli DP1200 Çeliğinde Lazer Kaynak Parametrelerinin Optimizasyonu” başlıklı yüksek lisans tezinden elde edilen veriler kullanılmıştır.

References

  • Altay, M. (2021). Otomotiv endüstrisinde kullanılan yüksek mukavemetli DP1200 çeliğinde lazer kaynak parametrelerinin optimizasyonu. (Yüksek Lisans Tezi), Bursa Uludağ Üniversitesi, Fen Bilimleri Enstitüsü Bursa, Türkiye.
  • Bandyopadhyay, K., Panda, S. K., & Saha, P. (2016). Optimization of fiber laser welding of DP980 steels using RSM to improve weld properties for formability. Journal of Materials Engineering and Performance, 25(6), 2462–2477. doi:10.1007/s11665-016-2071-y
  • Barzani, M. M., Zalnezhad, E., Sarhan, A., Farahany, S., Ramesh, S. (2015) Fuzzy logic based model for predicting surface roughness of machined Al-Si-Cu-Fe die casting alloy using different additives-turning. Measurement, 61, 150-161. doi: 10.1016/j.measurement.2014.10.003
  • Devendran, P., & Ashoka Varthanan, P. (2021). Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems. Materials Research Express, 8(12), 126524. doi:10.1088/2053-1591/ac432a
  • Heidarzadeh, A., Testik, Ö. M., Güleryüz, G., & Barenji, R. V. (2020). Development of a fuzzy logic based model to elucidate the effect of FSW parameters on the ultimate tensile strength and elongation of pure copper joints. Journal of Manufacturing Processes, 53, 250–259. doi:10.1016/j.jmapro.2020.02.020
  • Janasekaran, S., Jamaludin, M. F., Yusof, F., Shukor, M. H. A., & Ariga, T. (2017a). Influence of BA4047 filler addition through Mamdani fuzzy logic optimization for double-sided T-joint welding of aluminum alloys using low-power fiber laser. International Journal of Advanced Manufacturing Technology, 93(5–8), 2133–2143. doi:10.1007/s00170-017-0695-1
  • Janasekaran, S., Yusof, F., Zin, H. M., Jamaludin, M. F., & Shukor, M. H. A. (2017b). A fuzzy logic-based prediction model for fracture force using low-power fiber laser beam welding. International Journal of Advanced Manufacturing Technology, 91, 3603–3610. doi:10.1007/s00170-017-0073-z
  • Kim, P. (2017). MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence. CA, USA: Apres. doi:10.1007/978-1-4842-2845-6
  • Lin, J., Zhang, J., Min, J., Sun, C., & Yang, S. (2021). Laser-assisted conduction joining of carbon fiber reinforced sheet molding compound to dual-phase steel by a polycarbonate interlayer. Optics and Laser Technology, 133, 106561. doi:10.1016/j.optlastec.2020.106561
  • Medhi, T., Hussain, S. A. I., Saha Roy, B., & Saha, S. C. (2020). Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: A novel MCDM amalgamated MORSM approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(10), 1–22. doi:10.1007/s40430-020-02631-9
  • Nair, A., Ramji, V., Durai Raj, R., & Veeramani, R. (2020). Laser cladding of Stellite 6 on EN8 steel – A fuzzy modelling approach. Materials Today: Proceedings, 39, 348–353. doi:10.1016/j.matpr.2020.07.431
  • Palani, K., Elanchezhian, C., Ramnath, B. V., & Ramadoss, R. (2020). Hybrid Fuzzy based response surface optimization of welding parameters on Vickers microhardness and impact strength of FSWed AA8011-H24 aluminium alloy joints. Materials Today: Proceedings, 23, 573–582. doi:10.1016/j.matpr.2019.05.412
  • Rout, A., Deepak, B. B. V. L., Biswal, B. B., & Mahanta, G. B. (2022). Weld seam detection, finding, and setting of process parameters for varying weld gap by the utilization of laser and vision sensor in robotic arc welding. IEEE Transactions on Industrial Electronics, 69(1), 622–632. doi:10.1109/TIE.2021.3050368
  • Shanavas, S., & Dhas, J. E. R. (2018). Quality prediction of friction stir weld joints on a 5052 H32 aluminium alloy using fuzzy logic technique. Materials Today: Proceedings, 5(5), 12124 -12132. doi:10.1016/j.matpr.2018.02.190
  • Velázquez, D. R. T., Helleno, A. L., Fals, H. C., & dos Santos, R. G. (2021). Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference. International Journal of Advanced Manufacturing Technology, 115(5–6), 1547–1564. doi:10.1007/s00170-021-07269-y
  • Zeinali, M., & Khajepour, A. (2010). Development of an adaptive fuzzy logic-based inverse dynamic model for laser cladding process. Engineering Applications of Artificial Intelligence, 23(8), 1408-1419. doi:10.1016/j.engappai.2009.11.006
  • Zhang, L. L., Zhang, L. J., Long, J., Sun, X., Zhang, J. X., & Na, S. J. (2019). Enhanced mechanical performance of fusion zone in laser beam welding joint of molybdenum alloy due to solid carburizing. Materials and Design, 181, 107957. doi:10.1016/j.matdes.2019.107957

Prediction and Optimization of the Effect of Process Parameters on Mechanical and Geometric Properties in Laser Welding Process of DP1200 Steel by Fuzzy Logic Method

Year 2023, , 299 - 316, 30.04.2023
https://doi.org/10.53433/yyufbed.1195234

Abstract

In this study, DP 1200 steel sheets were joined in an overlap configuration by the fiber laser welding method. Welding geometry and mechanical properties of welded joints have changed according to laser power, scanning speed and laser incidence angle process parameters. In order to observe the effects of the parameters, an experimental design was created and experiments were carried out according to the Response Surface Methodology. Weld strength was evaluated by tensile tests and shear force values were obtained; the fully bonding distance was measured in the weld geometry, which is a criterion for weld quality. Experimental results were used in the Fuzzy Logic model according to the Mamdani method in predicting shear force and bonding distance, 27 rules were defined.Prediction results and experimental results were consistent with each other when compared. Optimum process parameters were determined as 2800 W laser power, 40 mm/s scanning speed and 70ᵒ laser incidence angle with the help of surface graphics.

Project Number

OUAP (MH)-2019-6

References

  • Altay, M. (2021). Otomotiv endüstrisinde kullanılan yüksek mukavemetli DP1200 çeliğinde lazer kaynak parametrelerinin optimizasyonu. (Yüksek Lisans Tezi), Bursa Uludağ Üniversitesi, Fen Bilimleri Enstitüsü Bursa, Türkiye.
  • Bandyopadhyay, K., Panda, S. K., & Saha, P. (2016). Optimization of fiber laser welding of DP980 steels using RSM to improve weld properties for formability. Journal of Materials Engineering and Performance, 25(6), 2462–2477. doi:10.1007/s11665-016-2071-y
  • Barzani, M. M., Zalnezhad, E., Sarhan, A., Farahany, S., Ramesh, S. (2015) Fuzzy logic based model for predicting surface roughness of machined Al-Si-Cu-Fe die casting alloy using different additives-turning. Measurement, 61, 150-161. doi: 10.1016/j.measurement.2014.10.003
  • Devendran, P., & Ashoka Varthanan, P. (2021). Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems. Materials Research Express, 8(12), 126524. doi:10.1088/2053-1591/ac432a
  • Heidarzadeh, A., Testik, Ö. M., Güleryüz, G., & Barenji, R. V. (2020). Development of a fuzzy logic based model to elucidate the effect of FSW parameters on the ultimate tensile strength and elongation of pure copper joints. Journal of Manufacturing Processes, 53, 250–259. doi:10.1016/j.jmapro.2020.02.020
  • Janasekaran, S., Jamaludin, M. F., Yusof, F., Shukor, M. H. A., & Ariga, T. (2017a). Influence of BA4047 filler addition through Mamdani fuzzy logic optimization for double-sided T-joint welding of aluminum alloys using low-power fiber laser. International Journal of Advanced Manufacturing Technology, 93(5–8), 2133–2143. doi:10.1007/s00170-017-0695-1
  • Janasekaran, S., Yusof, F., Zin, H. M., Jamaludin, M. F., & Shukor, M. H. A. (2017b). A fuzzy logic-based prediction model for fracture force using low-power fiber laser beam welding. International Journal of Advanced Manufacturing Technology, 91, 3603–3610. doi:10.1007/s00170-017-0073-z
  • Kim, P. (2017). MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence. CA, USA: Apres. doi:10.1007/978-1-4842-2845-6
  • Lin, J., Zhang, J., Min, J., Sun, C., & Yang, S. (2021). Laser-assisted conduction joining of carbon fiber reinforced sheet molding compound to dual-phase steel by a polycarbonate interlayer. Optics and Laser Technology, 133, 106561. doi:10.1016/j.optlastec.2020.106561
  • Medhi, T., Hussain, S. A. I., Saha Roy, B., & Saha, S. C. (2020). Selection of best process parameters for friction stir welded dissimilar Al-Cu alloy: A novel MCDM amalgamated MORSM approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(10), 1–22. doi:10.1007/s40430-020-02631-9
  • Nair, A., Ramji, V., Durai Raj, R., & Veeramani, R. (2020). Laser cladding of Stellite 6 on EN8 steel – A fuzzy modelling approach. Materials Today: Proceedings, 39, 348–353. doi:10.1016/j.matpr.2020.07.431
  • Palani, K., Elanchezhian, C., Ramnath, B. V., & Ramadoss, R. (2020). Hybrid Fuzzy based response surface optimization of welding parameters on Vickers microhardness and impact strength of FSWed AA8011-H24 aluminium alloy joints. Materials Today: Proceedings, 23, 573–582. doi:10.1016/j.matpr.2019.05.412
  • Rout, A., Deepak, B. B. V. L., Biswal, B. B., & Mahanta, G. B. (2022). Weld seam detection, finding, and setting of process parameters for varying weld gap by the utilization of laser and vision sensor in robotic arc welding. IEEE Transactions on Industrial Electronics, 69(1), 622–632. doi:10.1109/TIE.2021.3050368
  • Shanavas, S., & Dhas, J. E. R. (2018). Quality prediction of friction stir weld joints on a 5052 H32 aluminium alloy using fuzzy logic technique. Materials Today: Proceedings, 5(5), 12124 -12132. doi:10.1016/j.matpr.2018.02.190
  • Velázquez, D. R. T., Helleno, A. L., Fals, H. C., & dos Santos, R. G. (2021). Prediction of geometrical characteristics and process parameter optimization of laser deposition AISI 316 steel using fuzzy inference. International Journal of Advanced Manufacturing Technology, 115(5–6), 1547–1564. doi:10.1007/s00170-021-07269-y
  • Zeinali, M., & Khajepour, A. (2010). Development of an adaptive fuzzy logic-based inverse dynamic model for laser cladding process. Engineering Applications of Artificial Intelligence, 23(8), 1408-1419. doi:10.1016/j.engappai.2009.11.006
  • Zhang, L. L., Zhang, L. J., Long, J., Sun, X., Zhang, J. X., & Na, S. J. (2019). Enhanced mechanical performance of fusion zone in laser beam welding joint of molybdenum alloy due to solid carburizing. Materials and Design, 181, 107957. doi:10.1016/j.matdes.2019.107957
There are 17 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Meryem Altay 0000-0001-6930-6292

Hakan Aydın 0000-0001-7364-6281

Project Number OUAP (MH)-2019-6
Early Pub Date April 29, 2023
Publication Date April 30, 2023
Submission Date October 27, 2022
Published in Issue Year 2023

Cite

APA Altay, M., & Aydın, H. (2023). DP1200 Çeliği Lazer Kaynak İşleminde Proses Parametrelerinin Mekanik ve Geometrik Özelliklere Etkisinin Bulanık Mantık Yöntemi ile Tahmini ve Optimizasyonu. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 28(1), 299-316. https://doi.org/10.53433/yyufbed.1195234