Proper Arc Welding Condition Derivation of Auto-body Steel by Artificial Neural Network

Cho, Jungho

doi:2014.32.2.169

J Weld Join > Volume 32(2); 2014 > Article

Research Paper

Journal of Welding and Joining 2014;32(2):169-173.
DOI: https://doi.org/10.5781/JWJ.2014.32.2.43 Published online May 14, 2014.

Proper Arc Welding Condition Derivation of Auto-body Steel by Artificial Neural Network

Jungho Cho

Correspondence: Jungho Cho,
Email: junghocho@chungbuk.ac.kr

Abstract

Famous artificial neural network (ANN) is applied to predict proper process window of arc welding. Target weldment is variously combined lap joint fillet welding of automotive steel plates. ANN’s system variable such as number of hidden layers, perceptrons and transfer function are carefully selected through case by case test. Input variables are welding condition and steel plate combination, for example, welding machine type, shield gas composition, current, speed and strength, thickness of base material. The number of each input variable referred in welding experiment is counted and provided to make it possible to presume the qualitative precision and limit of prediction. One of experimental process windows is excluded for predictability estimation and the rest are applied for neural network training. As expected from basic ANN theory, experimental condition composed of frequently referred input variables showed relatively more precise prediction while rarely referred set showed poorer result. As conclusion, application of ANN to arc welding process window derivation showed comparatively practical feasibility while it still needs more training for higher precision.

Key Words: Artificial neural network, Arc welding, Automotive, Predictio