相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。An artificial neural network approach for tool path generation in incremental sheet metal free-forming
Christoph Hartmann et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2019)
Machine-learning based temperature- and rate-dependent plasticity model: Application to analysis of fracture experiments on DP steel
Xueyang Li et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2019)
Learning Corrections for Hyperelastic Models From Data
David Gonzalez et al.
FRONTIERS IN MATERIALS (2019)
Comparing Metamodeling Techniques For Variability Analysis In Sheet Metal Forming Processes
P. A. Prates et al.
PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING (ESAFORM 2019) (2019)
FEM-based neural network modeling of laser-assisted bending
Besufekad N. Fetene et al.
NEURAL COMPUTING & APPLICATIONS (2018)
Numerical study on the effect of mechanical properties variability in sheet metal forming processes
P. A. Prates et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)
Classification and selection of sheet forming processes with machine learning
Elia Hamouche et al.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING (2018)
Comprehensive prediction method for die-roll height of fine-blanking components
Xincun Zhuang et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)
Forming and springback prediction in press brake air bending combining finite element analysis and neural networks
Sara S. Miranda et al.
JOURNAL OF STRAIN ANALYSIS FOR ENGINEERING DESIGN (2018)
Using artificial neural networks to model aluminium based sheet forming processes and tools details
N. Mekras
36TH IDDRG CONFERENCE - MATERIALS MODELLING AND TESTING FOR SHEET METAL FORMING (2017)
The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets
Arash Jenab et al.
MATERIALS & DESIGN (2016)
Stretching the limits of forming processes by robust optimization: A numerical and experimental demonstrator
J. H. Wiebenga et al.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2015)
Effect of material scatter on the plastic behavior and stretchability in sheet metal forming
J. H. Wiebenga et al.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2014)
Replacing FEA for sheet metal forming by surrogate modeling
Simon Wessing et al.
COGENT ENGINEERING (2014)
Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming
Fethi Abbassi et al.
MATERIALS & DESIGN (2013)
Prediction of springback in sheet metal components with holes on the bending area, using experiments, finite element and neural networks
V. Nasrollahi et al.
MATERIALS & DESIGN (2012)
Parameter identification of an elasto-plastic behaviour using artificial neural networks-genetic algorithm method
Hamdi Aguir et al.
MATERIALS & DESIGN (2011)
Springback control in sheet metal bending by laser-assisted bending: Experimental analysis, empirical and neural network modelling
A. Gisario et al.
OPTICS AND LASERS IN ENGINEERING (2011)
Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal
Zemin Fu et al.
MATERIALS & DESIGN (2010)
Prediction of springback in wipe-bending process of sheet metal using neural network
Recep Kazan et al.
MATERIALS & DESIGN (2009)
Algorithms and strategies for treatment of large deformation frictional contact in the numerical simulation of deep drawing process
M. C. Oliveira et al.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2008)
Response surface methodology for the rapid design of aluminum sheet metal forming parameters
H. Naceur et al.
MATERIALS & DESIGN (2008)
Identification of Constitutive Parameters using Hybrid ANN multi-objective optimization procedure
H. Aguir et al.
INTERNATIONAL JOURNAL OF MATERIAL FORMING (2008)
Springback prediction for sheet metal forming based on GA-ANN technology
Wenjuan Liu et al.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2007)
Incorporating neural network material models within finite element analysis for rheological behavior prediction
B. Scott Kessler et al.
JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME (2007)
A new approach of die shape optimization for sheet metal forming processes
XX Shi et al.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2004)
Studies on the prediction of springback in air vee bending of metallic sheets using an artificial neural network
MV Inamdar et al.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2000)
Using neural networks to predict bending angle of sheet metal formed by laser
PJ Cheng et al.
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE (2000)
Three-dimensional numerical simulation of the deep-drawing process using solid finite elements
LF Menezes et al.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY (2000)