Research graph
References from Machine learning-based identification of process deviations in deep drawing of a cylindrical cup. Local targets link to admitted publications; unresolved targets remain external evidence.
Recent developments and trends in sheet metal forming
10.3390/met10060779 · 2020 · External reference
Opportunities and challenges in metal forming for lightweighting: review and future work
10.1115/1.4047732 · 2020 · External reference
Metal forming beyond shaping: predicting and setting product properties
10.1016/j.cirp.2015.05.001 · 2015 · External reference
Uncertainty estimation using Gaussian error propagation in metal forming process simulation
10.1002/pamm.202200073 · 2023 · External reference
A non-associated constitutive model based on Yld2004-18p yield criterion and its applications on sheet metal forming analysis
10.1038/s41598-025-85270-1 · 2025 · External reference
Study of anisotropic behavior in sheet metal forming
10.3390/ma17092031 · 2024 · External reference
Identification of the optimal blank holder force through In-Line measurement of blank Draw-In in a deep drawing process
10.3390/jmmp7060190 · 2023 · External reference
Characterisation of a measurement system for reproducible friction testing on sheet metal under plane strain
10.1016/s0301-679x(99)00098-5 · 1999 · External reference
Manifold learning-assisted uncertainty quantification of system parameters in the fiber metal laminates hot forming process
10.1007/s10845-024-02343-0 · 2025 · External reference
The homogeneous chaos
10.2307/2371268 · 1938 · External reference
Meta-models for structural reliability and uncertainty quantification
2012 · External reference
Monte Carlo uncertainty propagation with the NIST uncertainty machine
10.1021/acs.jchemed.0c00096 · 2020 · External reference
A directed monte carlo solution of Linear stochastic Algebraic System of equations
10.1016/j.finel.2010.01.004 · 2010 · External reference
Uncertainty analysis of deep drawing using surrogate model based probabilistic method
10.1007/s00170-016-8436-4 · 2016 · External reference
A review of recent advances in surrogate models for uncertainty quantification of high-dimensional engineering applications
10.1016/j.cma.2024.117508 · 2025 · External reference
Application of artificial intelligence techniques in incremental forming: a state-of-the-art review
10.1007/s10845-021-01868-y · 2023 · External reference
Machine learning in additive manufacturing: enhancing design, manufacturing and performance prediction intelligence
2025 · External reference
Unsupervised deep learning for advanced forming limit analysis in sheet metal: a tensile test-based approach
10.3390/ma16217001 · 2023 · External reference
Convolutional neural networks for prediction of geometrical errors in incremental sheet metal forming
10.1007/s10845-022-01932-1 · 2023 · External reference
Predicting Wall thickness changes in cold forging processes: an integrated FEM and neural network approach
2025 · External reference
The use of machine-learning techniques in material constitutive modelling for metal forming processes
10.3390/met12030427 · 2022 · External reference
Predicting edge cracking in sheet metal forming: evaluating machine learning models and data transformations
10.1007/s00170-025-15721-6 · 2025 · External reference
Machine learning-based forward and inverse designs for prediction and optimization of fracture toughness of aluminum alloy
10.1016/j.rineng.2024.102717 · 2024 · External reference
Machine learning application in predicting the properties of Ti-6Al-4V samples produced with power bed fusion technology
10.1016/j.rineng.2025.107404 · 2025 · External reference
Unresolved reference
2024 · External reference
Bayesian calibration of multiple coupled simulation models for metal additive manufacturing: a bayesian network approach
2022 · External reference
A benchmark on uncertainty quantification for deep learning prognostics
10.1016/j.ress.2024.110513 · 2025 · External reference
Deep learning in sheet metal bending with a novel theory-guided deep neural network
10.1109/jas.2021.1003871 · 2021 · External reference
Utilizing pretrained convolutional neural networks for crack detection and geometric feature recognition in concrete surface images
2024 · External reference
An experimental, computational, and statistical strategy for the bayesian calibration of complex material models
10.1007/s11831-023-09888-y · 2023 · External reference
Analysis of ESAFORM 2021 cup drawing benchmark of an Al alloy, critical factors for accuracy and efficiency of FE simulations
2022 · External reference
Plastic instability under plane stress
10.1016/0022-5096(52)90002-1 · 1952 · External reference
Unresolved reference
1948 · External reference
Development of accurate numerical methodologies applied to the stamping of advanced high strength steels and experimental validation
2020 · External reference
Uncertainty influence on the deep drawing of a cylindrical cup
10.1016/j.ijsolstr.2026.113927 · 2026 · External reference
Unresolved reference
2024 · External reference
On the distribution of points in a cube and the approximate evaluation of integrals
10.1016/0041-5553(67)90144-9 · 1967 · External reference
Tool path planning of consecutive free-form sheet metal stamping with deep learning
10.1016/j.jmatprotec.2022.117530 · 2022 · External reference
Unresolved reference
1994 · External reference
Unresolved reference
2019 · External reference
Structured light 3D scanning
2017 · External reference
In-situ-measurement of the friction coefficient in the deep drawing process
10.1088/1742-6596/896/1/012027 · 2017 · External reference
Friction laws dedicated to the numerical simulation of deep drawing
10.1051/metal:2002188 · 2002 · External reference
