Prediction of gas metal arc welding parameters based oil artificial neural networks


ATEŞ H.

MATERIALS & DESIGN, vol.28, no.7, pp.2015-2023, 2007 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 7
  • Publication Date: 2007
  • Doi Number: 10.1016/j.matdes.2006.06.013
  • Title of Journal : MATERIALS & DESIGN
  • Page Numbers: pp.2015-2023

Abstract

This paper presents a novel technique based on artificial neural networks (ANNs) for prediction of gas metal arc welding parameters. Input parameters of the model consist of gas mixtures, whereas, outputs of the ANN model include mechanical properties such as tensile strength, impact strength, elongation and weld metal hardness, respectively. ANN controller was trained with the extended delta-bardelta learning algorithm. The measured and calculated data were simulated by a computer program. The results showed that the outcomes of the calculation were in good agreement with the measured data, indicating that the novel technique presented in this work shows the good performance of the ANN model. (c) 2006 Published by Elsevier Ltd.