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Toward an intelligent aluminum laser welded blanks (ALWBs) factory based on industry 4.0; a critical review and novel smart model
10.1016/j.optlastec.2023.109661 · doi-reference
Attention-based multi-fidelity deep neural network for efficient estimation of welding residual stresses in V-shaped butt-welded high strength steel plate
10.1016/j.eswa.2024.126137 · doi-reference
Category-guided graph convolution network for semantic segmentation
10.1109/tnse.2024.3448609 · doi-reference
Optimal energy management of buildings using neural network-based thermal prediction and economic model predictive control
10.1016/j.aei.2025.104278 · doi-reference
Stressgan: A generative deep learning model for two-dimensional stress distribution prediction
10.1115/1.4049805 · doi-reference
Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks
10.1016/j.mechmat.2021.104191 · doi-reference
Machine learning-guided study of residual stress, distortion, and peak temperature in stainless steel laser welding
10.1007/s00339-024-08145-8 · doi-reference
Physics-guided machine learning and process analysis for residual stress prediction in circumferential welded SUS316L pipelines
10.1016/j.measurement.2025.119600 · doi-reference
Particle swarm optimization: A comprehensive survey
10.1109/access.2022.3142859 · doi-reference
Neuro evolutionary model for weld residual stress prediction
10.1016/j.asoc.2013.08.019 · doi-reference
Evolutionary fuzzy SVR modeling of weld residual stress
10.1016/j.asoc.2016.01.050 · doi-reference
Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems
10.1016/j.asoc.2018.05.017 · doi-reference
Residual stress prediction of arc welded austenitic pipes with artificial neural network ensemble using experimental data
10.1016/j.ijpvp.2023.104954 · doi-reference
A probabilistic graphical model foundation for enabling predictive digital twins at scale
10.1038/s43588-021-00069-0 · doi-reference
POD-based model reduction with empirical interpolation applied to nonlinear elasticity
10.1002/nme.5177 · doi-reference
Prediction of residual stresses in welded structures based on neural network: a review
10.1007/s10853-024-10178-6 · doi-reference
Machine learning applications in welding processes: progresses and opportunities
10.1016/j.ijmachtools.2025.104344 · doi-reference
Predicting welding residual stresses in a dissimilar metal girth welded pipe using 3D finite element model with a simplified heat source
10.1016/j.nucengdes.2010.11.010 · doi-reference
Numerical and experimental investigation on the weld-induced deformation and residual stress in stiffened plates with brackets
10.1007/s00170-016-8347-4 · doi-reference
Simulation and experiment on residual stress and deflection of cruciform welded joints
10.1016/j.jcsr.2023.108023 · doi-reference
Numerical simulation of welding residual stresses in 6082-T6 thin aluminum alloy
10.1016/j.measurement.2024.114800 · doi-reference
Effects of heat source geometric parameters and arc efficiency on welding temperature field, residual stress, and distortion in thin-plate full-penetration welds
10.1007/s00170-018-2516-6 · doi-reference
Three-dimensional model for numerical analysis of thermal phenomena in laser–arc hybrid welding process
10.1016/j.ijheatmasstransfer.2011.07.010 · doi-reference
Measurement of through-thickness stresses using small holes
10.1243/0309324021514899 · doi-reference
Cross-sectional mapping of residual stresses by measuring the surface contour after a cut
10.1115/1.1345526 · doi-reference
Distortion and residual stresses in thick plate weld joint of austenitic stainless steel: Experiments and analysis
10.1016/j.jmatprotec.2020.116944 · doi-reference
Effect of metallurgical variables on the stress corrosion crack growth behaviour of AISI type 316LN stainless steel
10.1016/j.corsci.2009.12.031 · doi-reference
Effect of inhomogeneous microstructure on the deformation and fracture mechanisms of 316LN stainless steel multi-pass weld joint using small punch test
10.1016/j.jnucmat.2020.152239 · doi-reference