Experimental Study and Modeling of Friction Stir Welding Process of Aluminum ۱۱۰۰ Alloys, using Artificial Neural Network with Taguchi Method
Publish place: International Journal of Advanced Design and Manufacturing Technology، Vol: 9، Issue: 3
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
View: 139
This Paper With 11 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_ADMTL-9-3_003
تاریخ نمایه سازی: 18 اردیبهشت 1400
Abstract:
In this paper, the temperature distribution in workpiece and microstructure of welded zone in friction stir welding of aluminum ۱۱۰۰ alloys and the effect of the tool rotational speed on these parameters have investigated experimentally. Also feed forward back propagation neural network has been used to predict the temperature of the workpiece during the welding process by considering the process time and tool rotational speed as input parameters of the neural network. For this purpose, the Taguchi design of experiments has been used and the network with minimum mean squared error was selected. This way of neural network selection is very formal and effective than the existing methods. The selected network mean squared error with this approach is ۰.۰۰۰۳۸۸, its most differences with experimental inputs is ۰.۷۷۰۹۹۷ºC and its regression R values is ۰.۹۹۱۱۳. Also according to experimental results, increasing tool rotational speed leads to higher plastic deformation in materials and also causes increasing the friction between tool and workpiece which leads to higher workpiece temperature.
Keywords:
Authors
V. Zakeri Mehrabad
Teacher at Azad University of Tabriz, Mechanical Department
Ali Doniavi
Mechanical Engineering Department, Faculty of Engineering, Urmia University, Urmia, Iran
A. Gholipoor
Teacher at Azad University of Tabriz, Mechanical Department *Corresponding author
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 :