| تعداد نشریات | 24 |
| تعداد شمارهها | 912 |
| تعداد مقالات | 8,158 |
| تعداد مشاهده مقاله | 15,838,941 |
| تعداد دریافت فایل اصل مقاله | 13,333,887 |
Parameter Optimization in Resistance Spot Welding of AISI 1060 Steel Using Adaptive Neural Fuzzy Inference System and Sensitivity Analysis | ||
| Iranian Journal of Materials Forming | ||
| مقاله 5، دوره 8، شماره 4، دی 2021، صفحه 33-45 اصل مقاله (3.54 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/ijmf.2021.41376.1192 | ||
| نویسندگان | ||
| Mehdi Safari* ؛ Amir Hossein Rabiee؛ Vahid Tahmasbi | ||
| Department of Mechanical Engineering, Arak University of Technology, Arak, 38181-8411, Iran | ||
| چکیده | ||
| Resistance spot welding process of AISI 1060 steel has been experimentally investigated by studying the effects of welding current, electrode force, welding cycle and cooling cycle on tensile-shear strength. Using the response surface methodology, experimental tests are performed. An adaptive neural-fuzzy inference system is applied to model and predict the behavior of tensile-shear strength. Additionally, the optimal parameters of adaptive neural-fuzzy inference systems are obtained by the gray wolf optimization algorithm. For modeling the process behavior, the results of experiments have been employed for training (70% of data) and testing (30% of data) of the inference system. The results show that the applied network has been very successful in predicting the tensile-shear strength and the coefficient of determination and mean absolute percentage error for the test section data are 0.96 and 6.02%, respectively. This indicates the considerable accuracy of the employed model in the approximation of the desired outputs. After that, the effect of each input parameter on tensile-shear strength is quantitatively evaluated with the Sobol sensitivity analysis method. The results show that the tensile-shear strength of the joint rises by increasing the welding current and welding cycle and also decreasing the electrode force and cooling cycle. | ||
| کلیدواژهها | ||
| Resistance spot welding؛ AISI 1060 steel؛ Adaptive neural-fuzzy inference system؛ Gray wolf optimization algorithm؛ Sobol sensitivity analysis method | ||
| مراجع | ||
|
[1] Ó. Martín, P. De Tiedra, and M. San-Juan, Combined effect of resistance spot welding and precipitation hardening on tensile shear load bearing capacity of A286 superalloy, Materials Science and Engineering: A, 2017. 688 (2017) 309-314.
[2] J. Bi, J. Song, Q. Wei, Y. Zhang, Y. Li, Z. Luo, Characteristics of shunting in resistance spot welding for dissimilar unequal-thickness aluminum alloys under large thickness ratio, Materials & Design, 101 (2016) 226-235.
[3] M. Safari, H. Mostaan, H. Yadeghari Kh., D. Asgari, Effects of process parameters on tensile-shear strength and failure mode of resistance spot welds of AISI 201 stainless steel, The International Journal of Advanced Manufacturing Technology, 89(5-8) (2017) 1853-1863.
[4] M. Safari, H. Mostaan, Experimental investigation of the effects of process parameters on the strength of eutectoid steel (AISI 1075) sheet resistance spot welds, Metallurgical Research & Technology, 113(3) (2016) 305.
[5] M. Safari, H. Mostaan, A. Ghaderi, Dissimilar resistance spot welding of AISI 304 to AISI 409 stainless steels: mechanical properties and microstructural evolutions, Metallurgical Research & Technology, 115(6) (2018) 610.
[6] M. Safari, J. Joudaki, Experimental investigation of resistance spot welding of ultrathin IF steel sheets. International Journal of Iron & Steel Society of Iran, 16(1) (2019) 51-57.
[7] R. Kumar, J.S. Chohan, R. Goyal, P. Chauhan, Impact of process parameters of resistance spot welding on mechanical properties and micro hardness of stainless steel 304 weldments, International Journal of Structural Integrity, 2020.
[8] T. Chen, Z. Ling, M. Wang, L. Kong, Effect of a slightly concave electrode on resistance spot welding of Q&P1180 steel, Journal of Materials Processing Technology, 285 (2020) 116797.
[9] D. Zhao, M. Ivanov, Y. Wang, D. Liang, W. Du, Multi-objective optimization of the resistance spot welding process using a hybrid approach, Journal of Intelligent Manufacturing, (2020) 1-16.
[10] Y.J. Xia, Z.W. Su, Y.B. Li, L. Zhou, Y. Shen, Online quantitative evaluation of expulsion in resistance spot welding, Journal of Manufacturing Processes, 46 (2019) 34-43.
[11] J.H. Ordoñez, R.R. Ambriz, C. García, G. Plascencia, D. Jaramillo, Overloading effect on the fatigue strength in resistance spot welding joints of a DP980 steel, International Journal of Fatigue, 121 (2019) 163-171.
[12] J. Valera, V. Miguel, A. Martínez, J. Naranjo, M. Cañas, Optimization of electrical parameters in resistance spot welding of dissimilar joints of micro-alloyed steels TRIP sheets, Procedia Manufacturing, 13 (2017) 291-298.
[13] K. Vignesh, A.E. Perumal, P. Velmurugan, Optimization of resistance spot welding process parameters and microstructural examination for dissimilar welding of AISI 316L austenitic stainless steel and 2205 duplex stainless steel, The International Journal of Advanced Manufacturing Technology, 93(1) (2017) 455-465.
[14] X. Wan, Y. Wang, D. Zhao, Y. Huang, A comparison of two types of neural network for weld quality prediction in small scale resistance spot welding, Mechanical Systems and Signal Processing, 93 (2017) 634-644.
[15] Y. Cho, S. Rhee, Quality estimation of resistance spot welding by using pattern recognition with neural networks, IEEE Transactions on Instrumentation and Measurement, 53(2) (2004) 330-334.
[16 B.N. Panda, M.R. Babhubalendruni, B.B. Biswal, D.S. Rajput, Application of artificial intelligence methods to spot welding of commercial aluminum sheets (BS 1050), In Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Springer, New Delhi, 2015, pp. 21-32.
[17] J.S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems, man, and cybernetics, 23(3) (1993) 665-685.
[18] S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer, Advances in Engineering Software, 69 (2014) 46-61.
[19] I.M. Sobol, Sensitivity analysis for non-linear mathematical models, Mathematical Modelling and Computational Experiment, 1 (1993) 407-414.
[20] D.C. Montgomery, Design and analysis of experiments, John wiley & sons, 2017.
[21] Y. Kaya, N. Kahraman, The effects of electrode force, welding current and welding time on the resistance spot weldability of pure titanium, The International Journal of Advanced Manufacturing Technology, 60(1-4) (2012) 127-134.
[22] K. Zhou, L. Cai, Study on effect of electrode force on resistance spot welding process, Journal of applied physics, 116(8) (2014) 084902.
[23] M. Pouranvari, S. Marashi, Critical review of automotive steels spot welding: process, structure and properties, Science and Technology of Welding and Joining, 18(5) (2013) 361-403. | ||
|
آمار تعداد مشاهده مقاله: 509 تعداد دریافت فایل اصل مقاله: 479 |
||