Cross-Section Bead Image Prediction in Laser Keyhole Welding of AISI 1020 Steel Using Deep Learning Architectures

A deep learning model was applied for predicting a cross-sectional bead image from laser welding process parameters. The proposed model consists of two successive generators. The first generator produces a weld bead segmentation map from laser intensity and interaction time, which is subsequently translated into an optical microscopic (OM) image by the second generator. Both generators exhibit an encoder & x2013;decoder structure based on a convolutional neural network (CNN). In the second generator, a conditional generative adversarial network (cGAN) was additionally employed with multiscale discriminators and residual blocks, considering the size of the OM image. For a training dataset, laser welding experiments with AISI 1020 steel were conducted on a large process window using a 2 KW fiber laser, and a total of 39 process conditions were used for the training. High-resolution OM images were successfully generated, and the predicted bead shapes were reasonably accurate (R-Squared: 89.0 & x0025; for penetration depth, 93.6 & x0025; for weld bead area)

Expanding Explainability Horizons: A Unified Concept-Based System for Local, Global, and Misclassification Explanations

Explainability of intelligent models has been garnering increasing attention in recent years. Of the various explainability approaches, concept-based techniques are notable for utilizing a set of human-meaningful concepts instead of focusing on individual pixels. However, there is a scarcity of methods that consistently provide both local and global explanations. Moreover, most of the methods have no offer to explain misclassification cases. To address these challenges, our study follows a straightforward yet effective approach. We propose a unified concept-based system, which inputs a number of super-pixelated images into the networks, allowing them to learn better representations of the target's objects as well as the target's concepts. This method automatically learns, scores, and extracts local and global concepts. Our experiments revealed that, in addition to enhancing performance, the models could provide deeper insights into predictions and elucidate false classifications