Prediction of Arc Welding Quality through Artificial Neural Network

Cho, Jungho

doi:2013.31.3.44

J Weld Join > Volume 31(3); 2013 > Article

Research Paper

Journal of KWJS 2013;31(3):44-48.
DOI: https://doi.org/10.5781/KWJS.2013.31.3.44 Published online June 30, 2013.

Prediction of Arc Welding Quality through Artificial Neural Network

Jungho Cho

Correspondence: Jungho Cho,
Email: Junghocho@cbnu.ac.kr

Abstract

Artificial neural network (ANN) model is applied to predict arc welding process window for automotive steel plate. Target weldment was various automotive steel plate combination with lap fillet joint. The accuracy of prediction was evaluated through comparison experimental result to ANN simulation. The effect of ANN variables on the accuracy is investigated such as number of hidden layers, perceptrons and transfer function type. A static back propagation model is established and tested. The result shows comparatively accurate predictability of the suggested ANN model. However, it restricts to use nonlinear transfer function instead of linear type and suggests only one single hidden layer rather than multiple ones to get better accuracy. In addition to this, obvious fact is affirmed again that the more perceptrons guarantee the better accuracy under the precondition that there are enough experimental database to train the neural network.

Key Words: Arc welding, Neural network, Prediction, Automotive, Steel