شبیه‌سازی حرارتی جوشکاری اصطکاکی اغتشاشی اتصال غیرمشابه فولاد زنگ‌نزن 304 به آلومینیوم 5083
نویسنده
دانشکده مهندسی مواد، دانشگاه صنعتی اصفهان، اصفهان
چکیده
جوشکاری اصطکاکی اغتشاشی از روش‌های بسیار کاربردی در اتصال فلزات غیرمشابه است. در این پژوهش به شبیه‌سازی توزیع حرارت در جوشکاری اصطکاکی اغتشاشی اتصال غیرمشابه فولاد زنگ‌نزن 304 به آلیاژ آلومینیوم 5083 با استفاده از روش المان محدود پرداخته شده است. حل حرارتی برای این مسئله از دو روش حل حالت پایدار و نیز حل حالت گذرا استفاده شده و دو روش با یکدیگر مقایسه شده‌اند. قابل ذکر است که، به‌منظور اعتبارسنجی مدل، ورق‌های فولاد زنگ‌نزن و آلومینیوم آماده‌سازی شده و جوشکاری اصطکاکی اغتشاشی صورت گرفته است. به‌علاوه با به‌کار بردن ترموکوپل بر قطعات، تاریخچه دمایی نقاط حین جوشکاری برای دو فلز به‌دست آمد. پس از آن، نتایج حاصل از شبیه‌سازی حرارتی با نتایج آزمایشگاهی حاصل از جوشکاری اصطکاکی اغتشاشی این اتصال مقایسه و اعتبارسنجی شد. در پایان با ساخت یک مدل شبکه عصبی، تأثیر پارامترهای ورودی فرایند جوشکاری بر حداکثر دمای زیر ابزار بررسی شد.
کلیدواژه‌ها
عنوان مقاله [English]
Thermal Simulation of Friction Stir Welding in 304 Stainless Steel to 5083 Aluminum Dissimilar Joint
نویسنده [English]
- B. Sadeghian
Friction stir welding is of the most applicable methods to join dissimilar metals. In this study, the thermal distribution during the joining of 304 stainless steel and 5083 aluminum alloy by friction stir welding method was simulated by the finite element method. Both, transient and stationary thermal solutions were used in the simulations and the two methods were compared correspondingly. To verify the model, two sheets of stainless steel and aluminum were prepared and the friction stir welding was applied. Additionally, by using thermocouples temperature, the history of points on the sheets was obtained during welding. Then, the simulation and the experimental results were compared to validate the model. Finally, an artificial neural network model was created and the effect of different input parameters on the maximum temperature under the tool was investigated.
کلیدواژه‌ها [English]
- Friction stir welding
- finite element method
- Dissimilar joint
- Transient solution
- Stationary solution
- Artificial Neural Network
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5. Khandkar, M. Z. H., Khan, J. A., Reynolds, A. P., and Sutton, M. A., “Predicting Residual Thermal Stresses in Friction Stir Welded Metals”, Journal of Materials Processing Technology, Vol. 174, No. 1, pp. 195-203, 2006.
6. Nandan, R., Roy, G., Lienert T., and DebRoy, T., “Numerical Modelling of 3D Plastic Flow and Heat Transfer During Friction Stir Welding of Stainless Steel”, Science and Technology of Welding & Joining, Vol. 11, No. 5, pp. 526-537, 2006.
7. Darvazi, A. R., and Iranmanesh, M., “Prediction of Asymmetric Transient Temperature and Longitudinal Residual Stress in Friction Stir Welding of 304L Stainless Steel”, Materials & Design, Vol. 55, No., pp. 812-820, 2014.
8. Bang, H., Jeon, G., Oh, I., and Ro, C., “Gas Tungsten Arc Welding Assisted Hybrid Friction Stir Welding of Dissimilar Materials Al6061-T6 Aluminum Alloy and STS304 Stainless Steel”, Materials & Design, Vol. 37, pp. 48-55, 2012.
9. Dong, H., Yang, L., Dong, C., and Kou, S., “Arc Joining of Aluminum Alloy to Stainless Steel with Flux-Cored Zn-Based Filler Metal”, Materials Science and Engineering: A, Vol. 527, No. 26, pp. 7151-7154, 2010.
10. Liu, X., Shuhuai, L., and Jun, N. “Analysis of Process Parameters Effects on Friction Stir Welding of Dissimilar Aluminum Alloy to Advanced High Strength Steel”, Materials & Design, Vol. 59, pp. 50-62, 2014.
11. Kim, D., Badarinarayan, H., Kim, J. H., Kim, C., Okamoto, K., Wagoner, R., and Chung, K., “Numerical Simulation of Friction Stir Butt Welding Process for AA5083-H18 Sheets”, European Journal of Mechanics-A/Solids, Vol. 29, No. 2, pp. 204-215, 2010.
12. Zhu, X. K., and Chao, Y. J., “Effects of Temperature-Dependent Material Properties on Welding Simulation”, Computers & Structures, Vol. 80, No. 11, pp. 967-976, 2002.
13. Coelho, R. S., Kostkac, A., dos Santosd, J. F., and Kaysser-Pyzallaab, A., “Friction-Stir Dissimilar Welding of Aluminium Alloy to High Strength Steels: Mechanical Properties and Their Relation to Microstructure”, Materials Science and Engineering: A, Vol. 556, pp. 175-183, 2012.
14. Hagan, M. T., Demuth, B., H., and Beale, M. H.. Neural Network Design. Boston. Pws Pub., 1996.
15. Toktas, I., and Zdemir, A. T., “Artificial Neural Networks Solution to Display Residual Hoop Stress Field Encircling a Split-Sleeve Cold Expanded Aircraft Fastener Hole”, Expert Systems with Applications, Vol. 38, pp. 553-563, 2011.
16. Hentschel, J., and Justin, L., Using Neural Nets to Recognize Handwriting, Northwestern University, EECS 395: Machine Learning.
17. Ogura, T., Saito, Y., Nishida, T., Nishida, H., Yoshida, T., Omichi, N., Fujimoto, M., and Hirose, A., “Partitioning Evaluation of Mechanical Properties and the Interfacial Microstructure in a Friction Stir Welded Aluminum Alloy/Stainless Steel Lap Joint”, Scripta Materialia, Vol. 66, No. 8, pp. 531-534, 2012.
18. Fukumoto, S., Tsubakino, H., Okita, K., and Tomita, T., “Amorphization by Friction Welding between 5052 Aluminum Alloy and 304 Stainless Steel”, Scripta Materialia, Vol. 42, No. 8, pp. 807-812, 2000.
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