Artificial neural network prediction of weld distortion rectification using a travelling induction coil

Barclay, C. J. and Campbell, S. W. and Galloway, A. M. and McPherson, N. A. (2013) Artificial neural network prediction of weld distortion rectification using a travelling induction coil. International Journal of Advanced Manufacturing Technology, 68 (1-4). pp. 127-140. ISSN 1433-3015 (https://doi.org/10.1007/s00170-012-4713-z)

[thumbnail of Galloway_AM_Pure_Artificial_Neural_Network_Prediction_of_Weld_Distortion_Rectification_using_a_Travelling_Induction_Coil_May_2012.pdf] PDF. Filename: Galloway_AM_Pure_Artificial_Neural_Network_Prediction_of_Weld_Distortion_Rectification_using_a_Travelling_Induction_Coil_May_2012.pdf
Preprint
Download (1MB)

Abstract

An experimental investigation has been carried out to determine the applicability of an induction heating process with a travelling induction coil for the rectification of angular welding distortion. The results obtained from experimentation have been used to create artificial neural network models with the ability to predict the welding induced distortion and the distortion rectification achieved using a travelling induction coil. The experimental results have shown the ability to reduce the angular distortion for 8 mm and 10 mm thick DH36 steel plate and effectively eliminate the distortion on 6 mm thick plate. Results for 6 mm plate also show the existence of a critical induction coil travel speed at which maximum corrective bending occurs. Artificial neural networks have demonstrated the ability to predict the final distortion of the plate after both welding and induction heating. The models have also been used as a tool to determine the optimum speed to minimise the resulting distortion of steel plate after being subjected to both welding and induction heating processes.

CORE (COnnecting REpositories)