An artificial neural network approach for parametric study on welding defect classification

Mei-Po Ho,Tai-Wing Chan,Hon-Wah Wai,Wing-Kit Ngai

doi:10.1007/s00170-022-08700-8

Abstract

In this paper, a welding defect prediction model has been developed and investigated through training an artificial neural network (ANN) based model. The input data were three welding process measurements (welding current, travel speed, and protective gas flow). The output data were non-destructive test results of respective weldments on four defect types (underfill, lack of penetration,incomplete fusion, and porosity) to ensure the consistency of the welding following the designed parameters; all data were obtained from 289 specimens produced by an automated GMAW welding manufacturing system. The 2-stages model comprises 13 inputs, hidden layers with 80–100 neurons and 4 outputs. The outputs were used to evaluate the classification accuracy in the confusion matrix for the prediction of weld quality. A further 73 specimens were used to test the accuracy of the trained ANN model. The model achieved 85% accuracy.

Highlights

MIG welding is one of the most widely used processes in shipbuilding, automobile manufacturing, and especially for section prefabrication in construction industry
The output data were non-destructive test results of respective weldments on four defect types (Underfill, Lack of penetration, Incomplete fusion, and Porosity) to ensure the consistency of the welding following the designed parameters, all data were obtained from 289 specimens produced by an automated MIG welding manufacturing system
A further 73 specimens were used to test the accuracy of the trained Artificial Neural Network (ANN) model

Summary

Introduction

MIG welding is one of the most widely used processes in shipbuilding, automobile manufacturing, and especially for section prefabrication in construction industry. Welded joint inspection is still needed to be undertaken by the skilled welder/ inspector Both destructive testing (DT) and nondestructive testing (NDT) are used for quality control of the welding in the construction industry. Visual surface inspection is needed for every welded joint, ultrasonic inspection and eddy current testing are employed to test on-site Other testing such as acoustic emission, magnetic particle inspection and cross-sectional inspection through an optical microscope and radiographic imaging are used frequently as well in the laboratory for welded samples. These tests are known as a manual operation which is time-consuming and labor-intensive. With the increase of application of robotic welding, a high requirement of automated, systematic and speedy sample quality checking is put forward

Methods

Results

Conclusion