پیش بینی و بهینه سازی هندسه جوش در فرایند جوشکاری قوس الکتریکی با گاز محافظ با استفاده از دستگاه بردار پشتیبان حداقل مربعات

Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: Persian
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شناسه ملی سند علمی:

JR_JWSTI-8-2_020

تاریخ نمایه سازی: 18 اردیبهشت 1402

Abstract:

In Wire and arc additive manufacturing (WAAM) based on Gas metal arc welding (GMAW) is one of the methods of manufacturing metal layer by layer. One of this method's basic steps is predicting the welding geometry created in each welding step. In the current research, an experimental study was conducted in this field considering the effective parameters of welding geometry. For this purpose, three parameters of voltage, welding speed, and wire feeding speed were considered as effective parameters on the welding geometry of the process. The width and height of the weld bead was selected as the answer according to the type and application of the research. The least squares support vector machine was used to model the welding geometry in the process. The results obtained from the regression (R۲) of train, test, validation, and total were ۰.۹۴۵, ۰.۷۹۳, ۰.۸۹۴, and ۰.۸۸۱ respectively. The comparison between the experimental data and the model data shows the significance of the proposed model.

Keywords:

Wire and arc additive manufacturing , gas metal arc welding , welding geometry , least squares support vector machine , modeling , ساخت افزایشی قوس و سیم , جوشکاری قوس الکتریکی با گاز محافظ , هندسه جوش , ماشین بردار پشتیبان حداقل مربعات , مدل-سازی

Authors

محمدرضا مرکی

Faculty of Materials and Metallurgy, Birjand University of Technology, Birjand, Iran

مسعود محمودی

Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

محمد یوسفیه

Faculty of Materials Engineering and Metallurgy, Semnan University, Semnan, Iran

هادی تقی ملک

Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

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  • W. Scientific Pub, C. Singapore, C. Shawe, J. Taylor., An ...
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  • D. Yang, C. He, G. Zhang., forming characteristics of thin-wall ...
  • J. Xiong, G. Zhang, J. Hu, L. Wu., Bead geometry ...
  • N. SV, Modern Welding Technology. New Delhi, Oxford and IBH ...
  • M.M. Anzehaee, M. Haeri., A new method to control heat ...
  • W.C. Lee, C.C. Wei, S.C. Chung., Development of a hybrid ...
  • J. Xiong, G. Zhang, J. Hu, L. Wu., Bead geometry ...
  • T. Saeheaw., Comparison of different supervised machine learning algorithms for ...
  • R.T. Martínez, G.A. Bestard, A.M.A. Silva, S.C.A. Alfaro., Analysis of ...
  • P. Wanjara, M. Brochu, M. Jahazi., Electron beam free-forming of ...
  • Y. Song, S. Park, D. Choi, J. Haesung., ۳D welding ...
  • C. Cortes, V. Vapnik, Support-vector networks. Machine Learning, Vol. ۲۰, ...
  • V. Kecman., Learning and soft computing: support vector machines, neural ...
  • A. Aryafar, R. Gholami, R. Rooki, F. Ardejani, Heavy metal ...
  • F. Parrella, Online support vector regression. A thesis presented for ...
  • J. Suykens, T.V. Gestel, J.D. Brabanter, B.D. Moor, J. Vandewalle., ...
  • W. Scientific Pub, C. Singapore, C. Shawe, J. Taylor., An ...
  • H. Wang, D. Hu, Comparison of SVM and LS-SVM for ...
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