Minimalist Regression Network with Reinforced Gradients and Weighted Estimates: a Case Study on Parameters Estimation in Automated Welding
This work addresses parameter estimation in automated welding, offering a scalable solution for industrial applications, though it appears incremental in its methodological contributions.
The paper tackles the problem of estimating direct weld parameters in automated welding by proposing a minimalist neural regression network with reinforced gradients and weighted estimates, achieving significant improvement over state-of-the-art techniques and demonstrating scalability across different welding techniques.
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.