A Hybrid SVM-RVM Algorithm to Mechanical Properties in the Friction Stir Welding Process
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
View: 281
This Paper With 12 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JACM-8-1_004
تاریخ نمایه سازی: 8 آذر 1400
Abstract:
The friction stir welding method is one of the solid-state welding methods for non-homogeneous metals. In this study, the ۵XXX 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.
Keywords:
Authors
Hadi Tagimalek
Faculty of Mechanical Engineering, Semnan University, Semnan, Iran
Mohammad Reza Maraki
Department of Materials and Metallurgy Engineering, Birjand University of Technology, Birjand, Iran
Masoud Mahmoodi
Faculty of Mechanical Engineering, Semnan University, Semnan, Iran
Majid Azargoman
Faculty of Mechanical Engineering, Semnan University, Semnan, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :