آمار
| تعداد نشریات | 31 |
| تعداد شماره‌ها | 1,003 |
| تعداد مقالات | 8,914 |
| تعداد مشاهده مقاله | 9,672,416 |
| تعداد دریافت فایل اصل مقاله | 7,963,987 |
A Hybrid SVM-RVM Algorithm to Mechanical Properties in the Friction Stir Welding Process | ||
| Journal of Applied and Computational Mechanics | ||
| مقاله 4، دوره 8، شماره 1، فروردین 2022، صفحه 36-47 اصل مقاله (670.07 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22055/jacm.2019.31017.1811 | ||
| نویسندگان | ||
| Hadi Tagimalek* 1؛ Mohammad Reza Maraki2؛ Masoud Mahmoodi1؛ Majid Azargoman1 | ||
| 1Faculty of Mechanical Engineering, Semnan University, Semnan, Iran | ||
| 2Department of Materials and Metallurgy Engineering, Birjand University of Technology, Birjand, Iran | ||
| چکیده | ||
| The friction stir welding method is one of the solid-state welding methods for non-homogeneous metals. In this study, the 5XXX series aluminum sample and pure copper are subjected to four passes friction welding process and then the mechanical and metallurgical properties of the welded samples are compared with the prototype. For this purpose, the effect of welding parameters including rotational speed, forward speed and pin angle of the tool is tested by the full factorial method. In this process, hardness estimation and tensile testing are based on input process parameters in order to obtain mechanical properties is an important issue. For this purpose, a mathematical model of mechanical properties must be defined based on the input process parameters. Due to the complex nature of the effect of input process parameters on mechanical properties, this modeling is a complex mathematical problem in which the use of supervised learning algorithms is considered as an efficient alternative. In this paper, a new combination of Relevance Vector Machine (RVM) and Support Vector Machine (SVM) is presented which has a higher degree of accuracy. | ||
| کلیدواژه‌ها | ||
| Friction stir welding؛ Hardness؛ Support Vector Machine؛ Relevance Vector Machine | ||
| مراجع | ||
|
[1] Le, V.S., Ghiotti, A., Lucchetta, G., Preliminary studies on single point incremental forming for thermoplastic materials, International Journal of Material Forming, 1, 2008, 1179-1182.
[2] Bhamji, I., Moat, R.J., Preuss, M., Thread gill, P.L., Addison, A.C., Peel, M.J., Linear friction welding of aluminum to copper, Journal Science and Technology of Welding and Joining, 17, 2012, 314-320.
[3] Mishra, R.S., Ma, Z.Y., Friction stir welding and processing, Materials Science and Engineering: R: Reports, 50(1-2), 2005, 1-78.
[4] Ham, M., Jeswiet, J., Single point incremental forming and the forming criteria for AA3003, CIRP Annals Manufacturing Technology, 55, 2006, 241–244.
[5] Liu, Z., Li, Y., Meehan, P.A., Experimental investigation of mechanical properties formability and force measurement for AA7075-O aluminum alloy sheets formed by incremental forming, International Journal of Precision Engineering and Manufacturing, 14, 2013, 1891–1899.
[6] Esmaeili, A., Rajani, H.R.Z., Sharbati, M., Besharati Givi, M.K., Shamanian, M., The role of rotation speed on intermetallic compounds formation and mechanical behavior of friction stir welded brass/aluminum 1050 couple, Intermetallics, 19, 2011, 1711-1719.
[7] Mahoney, M.W., Rhodes, C.G., Flint off, J.G., Bingel, W.H., Spurling, R.A., Properties of friction stir welded 7075 T651 aluminum, Metallurgical, and Materials Transactions A, 29, 1998, 1955-1964.
[8] Echrif, S.B.M., Hrairi, M., Research and progress in incremental sheet forming processes, Materials and Manufacturing Processes, 26, 2011, 1404-1414.
[9] Jeswiet, J., Micari, F., Hirt, G., Bramley, A., Duflou, J., All wood, J., Asymmetric single point incremental forming of sheet metal, CIRP Annals Manufacturing Technology, 54, 2005, 88-114.
[10] Mehrabi, M., Sharifpur, M., Meyer, J.P., Viscosity of Nanofluids based on an artificial intelligence model, International communication in Heat and Mass Transfer, 54, 2013, 16-21.
[11] Cortes, C., Vapnik, V., A Novel Operational Partition between Neural Network Classifiers on Vulnerability to Data Mining Bias, Journal of Software Engineering and Applications, 7, 2014, 264-272.
[12] Agrawal, R.K., Muchahary, F., Tripathi, M.M., Ensemble of relevance vector machines and boosted trees for electricity price forecasting, Applied Energy,250, 2019, 540-548.
[13] Meyer, D., Leisch, F., Hornik, K., The support vector machine under test, Neurocomputing,55, 2003, 169-186.
[14] Kong, D., Chen, Y., Li, N., Duan, C., Lu, L., Chen, D., Relevance vector machine for tool wear prediction, Mechanical Systems, and Signal Processing,127, 2019, 573-594.
[15] Tipping, M. E., Sparse Bayesian learning and the relevance vector machine, Journal of Machine Learning Research, 1, 2001, 211-244.
[16] Gowthaman, P.S., Jeyakumar, S., A Review on machining of High-Temperature Aeronautics Super-alloys using WEDM, Materialstoday PROCEEDINGS, 18, 2019, 4782-4791.
[17] Yan, J., Liu, Y., Han, S., Qiu, M., Windpower grouping forecasts and its uncertainty analysis using optimized relevance vector machine, Renewable and Sustainable Energy Reviews, 27, 2013, 613-621.
[18] Feng, Z., Wang, R., Self-validating Pneumatic Actuator Fault Diagnosis Based on Relevance Vector Machine, International Journal of Control and Automation, 7, 2014, 181-200.
[19] Tipping, M.E., Bayesian inference: An introduction to principles and practice in machine learning, Advanced Lectures on Machine Learning, 3176, 2003, 41-62.
[20] Cheng, Y., Fang, H., A hybrid prognostic method for system degradation based on particle filter and relevance vector machine, Reliability Engineering & System Safety,186, 2019, 51-63.
[21] Ay, M., Stemmler, S., Schwenzer, M., Abel, D., Bergs, T., Model Predictive Control in Milling based on Support Vector Machines, IFAC-Papers Online, 52, 2019,1797-1802. | ||
|
آمار تعداد مشاهده مقاله: 897 تعداد دریافت فایل اصل مقاله: 868 |
||