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شبیه سازی پارامترهای فرآیند اکستروژن پیچشی آلیاژ آلومینیوم AA6061-T6 توسط شبکه عصبی مصنوعی | ||
| دوفصلنامه مهندسی شناورهای تندرو | ||
| دوره 21، شماره 60، شهریور 1401، صفحه 85-95 اصل مقاله (357.52 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| محمد خسروی* 1؛ ایمان طاهری دوست آباد2 | ||
| 1دانشگاه صنعتی بیرجند، بیرجند ، ایران | ||
| 2دانشگاه صنعتی بیرجند- بیرجند - ایران | ||
| تاریخ دریافت: 07 شهریور 1401، تاریخ بازنگری: 12 شهریور 1401، تاریخ پذیرش: 24 شهریور 1401 | ||
| چکیده | ||
| Modern fabrication is to a large extent based on deformation processing. Plastic deformation process is a technique capable of producing metal products with high strength and good ductility. Using the parameters of load, temperature and the number of passes in twist extrusion, it is possible to produce an alloy with good properties and characteristics. Plastic deformation of AA6061-T6 aluminum alloy by twist extrusion is an important issue. In this study, we investigated the effect of load, temperature and the number of passes of twist extrusion on AA6061-T6. Using the input and output data, the process was modeled by the neural network method. In order to train the neural network, Neuro Solution software was used and for reducing the mean square error, the gradient descent momentum algorithm was implemented. Results showed that the effect of the number of passes and the load on tensile strength and hardness were maximum and minimum respectively. | ||
| کلیدواژه‌ها | ||
| Twist extrusion؛ artificial neural network؛ the number of passes | ||
| عنوان مقاله [English] | ||
| Simulation of twist extrusion process parameters of AA6061-T6 aluminum alloy by artificial neural network | ||
| نویسندگان [English] | ||
| Mohammad Khosravi1؛ Iman Taheridoustabad2 | ||
| 1Birjand University of Technology | ||
| 2Birjand University of Technology | ||
| چکیده [English] | ||
| Modern fabrication is to a large extent based on deformation processing. Plastic deformation process is a technique capable of producing metal products with high strength and good ductility. Using the parameters of load, temperature and the number of passes in twist extrusion, it is possible to produce an alloy with good properties and characteristics. Plastic deformation of AA6061-T6 aluminum alloy by twist extrusion is an important issue. In this study, we investigated the effect of load, temperature and the number of passes of twist extrusion on AA6061-T6. Using the input and output data, the process was modeled by the neural network method. In order to train the neural network, Neuro Solution software was used and for reducing the mean square error, the gradient descent momentum algorithm was implemented. Results showed that the effect of the number of passes and the load on tensile strength and hardness were maximum and minimum respectively. | ||
| کلیدواژه‌ها [English] | ||
| Twist extrusion, artificial neural network, the number of passes | ||
| مراجع | ||
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[1] VALIEV, Ruslan Z.; STRAUMAL, Boris; LANGDON, Terence G. Using Severe Plastic Deformation to Produce Nanostructured Materials with Superior Properties. Annual Review of Materials Research, 2022, 52. [2] VALIEV, R. Z.; ALEKSANDROV, I. V. Nanostructured Materials Produced by Intense Plastic Deformation. Logos, Moscow, 2000. [3] EDALATI, Kaveh, et al. Nanomaterials by severe plastic deformation: review of historical developments and recent advances. Materials Research Letters, 2022, 10.4: 163-256. [4] WANG, Zhi-Rui, et al. A Review of Ultrafine-Grained Magnetic Materials Prepared by Using High-Pressure Torsion Method. Materials, 2022, 15.6: 2129. [5] BODKHE, Mohan G., et al. Finite element analysis of copper tube to study the behavior of equivalent strength during the severe plastic deformation process. Materials Today: Proceedings, 2022, 56: 3129-3136. [6] FAREGH, Sara Sadat Hosseini; HASSANI, Amir. Stress and strain distribution in twist extrusion of AA6063 aluminum alloy. International Journal of Material Forming, 2018, 11.2: 175-184. [7] ASGHAR, Seyed Ali; MOUSAVI, Akbari; BAHADOR, Shahab Ranjbar. Investigation and numerical analysis of strain distribution in the twist extrusion of pure aluminum. JOM, 2011, 63.2: 69-76. [8] IQBAL, U. Mohammed; KUMAR, VS Senthil. An analysis on effect of multipass twist extrusion process of AA6061 alloy. Materials & Design, 2013, 50: 946-953. [9] OKOKPUJIE, I. P.; TARTIBU, L. K. A mini-review of the behaviour characteristic of machining processes of aluminium alloys. Materials Today: Proceedings, 2022. [10] MOHAMMED, I. U.; SENTHIL, K. S. Application of Response Surface Methodology in Optimizing Process Parameters of Twist Extrusion Process for Aluminum Aa 6061-T6 Alloy. Measurement, 2016, 94.94: 126-138. [11] KUMAR, N., et al. The influence of metallurgical factors on low cycle fatigue behavior of ultra-fine grained 6082 Al alloy. International Journal of Fatigue, 2018, 110: 130-143. [12] HEYDARI, Farnam; SALJOGHI, Hamed; NOURBAKHSH, Sayed Hassan. Numerical Investigation of the Cross-section and Twist Extrusion Die Angle on the Distribution of Plastic Strain and Microstructure of Al7050 Alloy. Journal of Stress Analysis, 2020, 4.2: 1-8. [13] MOHAMMEDIQBAL, U.; SENTHILKUMAR, V. S. Experimental investigation and analysis of microstructure and mechanical properties on twist extrusion forming process of AA7075-T6 aluminium alloy. Int J Mech Mater Eng (IJMME), 2012, 7.1: 24-30. [14] VELMANIRAJAN, K., et al. Numerical modelling of aluminium sheets formability using response surface methodology. Materials & Design, 2012, 41: 239-254. [15] SENTHIL KUMAR, V. S.; MOHAMMED IQBAL, U. Effect of Process Parameters on the Microstructure Homogeneity of AA6082 Aluminum Alloy Deformed by Twist Extrusion. In: ASME International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. p. V02AT02A023. [16] ZENDEHDEL, H.; HASSANI, A. Influence of twist extrusion process on microstructure and mechanical properties of 6063 aluminum alloy. Materials & Design, 2012, 37: 13-18. [17] MOUSAVI, SAA Akbari; BAHADORI, Sh Ranjbar; SHAHAB, A. R. Numerical and experimental studies of the plastic strains distribution using subsequent direct extrusion after three twist extrusion passes. Materials Science and Engineering: A, 2010, 527.16-17: 3967-3974. [18] POURSAFAR, Amin, et al. Experimental and mathematical analysis on spring-back and bowing defects in cold roll forming process. International Journal on Interactive Design and Manufacturing (IJIDeM), 2022, 1-13. [19] PHAM, Cao Hung, et al. Mechanical properties and residual stresses in cold-rolled aluminium channel sections. Engineering structures, 2019, 199: 109562. [20] DE SANTANA GOMES, Wellison José. Structural reliability analysis using adaptive artificial neural networks. ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 2019, 5.4. [21] WANG, Linda; LIN, Zhong Qiu; WONG, Alexander. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 2020, 10.1: 1-12. [22] IQBAL, U. Mohammed; KUMAR, V. S. Effect of process parameters on microstructure and mechanical properties on severe plastic deformation process of AA7075-T6 aluminum alloy. In: Advanced Materials Research. Trans Tech Publications Ltd, 2013. p. 705-709. | ||
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آمار تعداد مشاهده مقاله: 109 تعداد دریافت فایل اصل مقاله: 124 |
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