AdditiveGDL: generative deep learning for predicting local thermal distributions in metal 3D-printed layers
- 10 July 2025
- journal article
- research article
- Published by Springer Nature in Journal of Intelligent Manufacturing
- Vol. 37 (6) , 2203-2214
Abstract
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Funding Information
- DEVCOM Army Research Laboratory (W911NF-20-2-0175)
This publication has 41 references indexed in Scilit:
- Development of an efficient multi-scale model to predict residual stresses and distortions in the laser powder bed fusion process for Inconel-718The International Journal of Advanced Manufacturing Technology, 2024
- Deep learning: systematic review, models, challenges, and research directionsNeural Computing and Applications, 2023
- Modified Inherent Strain Method for Predicting Residual Deformation and Stress in Metal Additive Manufacturing2023
- Multivariate Calibration and Experimental Validation of a 3D Finite Element Thermal Model for Laser Powder Bed Fusion Metal Additive ManufacturingIntegrating Materials and Manufacturing Innovation, 2018
- Influence of scan strategy and process parameters on microstructure and its optimization in additively manufactured nickel alloy 625 via laser powder bed fusionThe International Journal of Advanced Manufacturing Technology, 2016
- Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks2016
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- Heat Conduction Fundamentals2012
- Residual stress-driven delamination in deposited multi-layersInternational Journal of Solids and Structures, 1996
- Image registration by local approximation methodsImage and Vision Computing, 1988