Minimalist Regression Network with Reinforced Gradients and Weighted Estimates: a Case Study on Parameters Estimation in Automated Welding

TL;DR

This paper introduces a minimalist neural regression network with reinforced gradients and weighted estimates, demonstrating improved accuracy and scalability in weld parameter estimation across multiple welding techniques.

Contribution

It proposes a novel neural regression architecture with shared multiplicative parameters and adaptive weighting, enhancing estimation accuracy and robustness across diverse welding methods.

Findings

Significant improvement over existing methods in weld parameter estimation.

Model maintains performance across combined data from different welding techniques.

Demonstrates scalability and technique-independence in estimation quality.

Abstract

This paper presents a minimalist neural regression network as an aggregate of independent identical regression blocks that are trained simultaneously. Moreover, it introduces a new multiplicative parameter, shared by all the neural units of a given layer, to maintain the quality of its gradients. Furthermore, it increases its estimation accuracy via learning a weight factor whose quantity captures the redundancy between the estimated and actual values at each training iteration. We choose the estimation of the direct weld parameters of different welding techniques to show a significant improvement in calculation of these parameters by our model in contrast to state-of-the-arts techniques in the literature. Furthermore, we demonstrate the ability of our model to retain its performance when presented with combined data of different welding techniques. This is a nontrivial result in attaining an scalable model whose quality of estimation is independent of adopted welding techniques.