Estimation of Bead on Plate Geometry of Super Duplex Stainless Steel on Low Carbon Steel using Artificial Neural Networks

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Authors

  • Amit Kumar Hansda
     Mechanical Engineering Department Kalyani Government Engineering College Kalyani - 741235, West Bengal, India ,IN
  • Santanu Das
     Mechanical Engineering Department Kalyani Government Engineering College Kalyani - 741235, West Bengal, India ,IN

DOI:

https://doi.org/10.22486/iwj.v56i3.222952

Keywords:

Welding, FCAW, Bead on Plate welding, Super Duplex Stainless Steel, Neural Networks, ANN, Prediction.

Abstract

Bead on plate geometry gives a priori knowledge about weld characteristics. In the current work, bead on plate experimental data are taken from one previously published work and different training algorithms are applied to get trained with the experimental data. Experiments were done using four-factor, five-level central composite rotatable design with full replication technique using response surface methodology. The working range of each parameter was decided upon by inspecting the weld bead for smooth appearance and the absence of visible defects. Bead of Super Duplex Stainless Steel was deposited on low carbon steel substrate using flux cored arc welding. An attempt is made in this work to predict the bead geometry parameters using Artificial Neural Networks (ANN). Effectiveness of three different ANN training functions are compared to choose the best model of these three. TRAINLM (LevenbergMarquardt) algorithm is found to be the most appropriate training function for prediction of bead geometry in this work.

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Published

2023-07-01

Issue

Volume 56 ,Issue 3 , July 2023

Section

Research Articles

 

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