Real time error detection in metal arc welding process using Artificial Neural Netwroks

TL;DR

This paper presents a real-time error detection system for metal arc welding using neural networks, analyzing electrical parameters with time series methods and classifying errors with self-organizing maps and neural classifiers.

Contribution

It introduces a novel real-time error detection approach combining time series analysis, self-organizing maps, and neural network classifiers for welding quality assurance.

Findings

Multi Layer Perceptron achieved higher accuracy than Radial Basis Function.

The system effectively segregates desirable and undesirable weld patterns.

Real-time detection reduces post-inspection costs.

Abstract

Quality assurance in production line demands reliable weld joints. Human made errors is a major cause of faulty production. Promptly Identifying errors in the weld while welding is in progress will decrease the post inspection cost spent on the welding process. Electrical parameters generated during welding, could able to characterize the process efficiently. Parameter values are collected using high speed data acquisition system. Time series analysis tasks such as filtering, pattern recognition etc. are performed over the collected data. Filtering removes the unwanted noisy signal components and pattern recognition task segregate error patterns in the time series based upon similarity, which is performed by Self Organized mapping clustering algorithm. Welder quality is thus compared by detecting and counting number of error patterns appeared in his parametric time series. Moreover, Self Organized mapping algorithm provides the database in which patterns are segregated into two classes either desirable or undesirable. Database thus generated is used to train the classification algorithms, and thereby automating the real time error detection task. Multi Layer Perceptron and Radial basis function are the two classification algorithms used, and their performance has been compared based on metrics such as specificity, sensitivity, accuracy and time required in training.