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• W3016487788 abstract "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-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% for penetration depth, 93.6% for weld bead area)." @default.
• W3016487788 created "2020-04-24" @default.
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• W3016487788 date "2020-01-01" @default.
• W3016487788 modified "2023-09-30" @default.
• W3016487788 title "Cross-Section Bead Image Prediction in Laser Keyhole Welding of AISI 1020 Steel Using Deep Learning Architectures" @default.
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• W3016487788 doi "https://doi.org/10.1109/access.2020.2987858" @default.
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