Application of Deep Neural Network in Estimation of the Weld Bead Parameters

TL;DR

This paper introduces a deep neural network model for estimating weld bead parameters, demonstrating significant accuracy improvements and scalability across different welding techniques, challenging common assumptions in the field.

Contribution

The paper presents a novel deep neural network architecture for weld bead parameter estimation, showing improved accuracy and scalability across diverse welding datasets.

Findings

Significant reduction in estimation errors compared to existing methods

Model maintains accuracy across different welding techniques

Deep network outperforms traditional approaches in weld parameter estimation

Abstract

We present a deep learning approach to estimation of the bead parameters in welding tasks. Our model is based on a four-hidden-layer neural network architecture. More specifically, the first three hidden layers of this architecture utilize Sigmoid function to produce their respective intermediate outputs. On the other hand, the last hidden layer uses a linear transformation to generate the final output of this architecture. This transforms our deep network architecture from a classifier to a non-linear regression model. We compare the performance of our deep network with a selected number of results in the literature to show a considerable improvement in reducing the errors in estimation of these values. Furthermore, we show its scalability on estimating the weld bead parameters with same level of accuracy on combination of datasets that pertain to different welding techniques. This is a nontrivial result that is counter-intuitive to the general belief in this field of research.