ESAFORM 2022
25th International Conference on Material Forming
R. Lourenço1,a, A. Andrade-Campos1,b, P. Georgieva2,c
1Dept. of Mechanical Engineering
Centre for Mechanical Technology and Automation, University of Aveiro, Portugal
2Dept. of Electronics Telecommunications and Informatics
Institute of Electronics and Informatics Engineering, University of Aveiro, Portugal
a
rubenl@ua.pt, bgilac@ua.pt, cpetia@ua.pt
27-29 April, Braga, Portugal
RS 2022
Implicit material modelling using AI techniques
and big data generation
Rúben Lourenço
Supervisory team: A. Andrade-Campos, Pétia Georgieva
Doctoral Programme in Mechanical Engineering
TEMA – Centre for Mechanical Technology and Automation
Towards virtual forming and AI Implicit material modelling using AI technique...vformxsteels
RS 2022
Rúben Lourenço
Supervisory team: A. Andrade-Campos, Pétia Georgieva
Doctoral Programme in Mechanical Engineering
TEMA – Centre for Mechanical Technology and Automation
Parameter Identification of Swift law using a FEMU-based approach and an inno...vformxsteels
ESAFORM 2022
M. Conde1, J. Henriques1,
S. Coppieters2, A. Andrade-Campos1
1 TEMA, Department of Mechanical Engineering, University of Aveiro, Portugal
2 Department of Materials Engineering, Ghent Technology Campus, KU Leuven, Belgium
Parameter Identification of Swift law using a FEMU-based approach and an inno...vformxsteels
ESAFORM 2022
M. Conde1, J. Henriques1,
S. Coppieters2, A. Andrade-Campos1
1 TEMA, Department of Mechanical Engineering, University of Aveiro, Portugal
2 Department of Materials Engineering, Ghent Technology Campus, KU Leuven, Belgium
Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Att...ShuvamRoy12
Roy, A. and Bagade, P. (2024). Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 492-499.
Assessment of likely consequences of a potential accident is a major concern for loss prevention and
safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial
sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a
difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler
models are used but remain inaccurate especially when turbulence is heterogeneous. The present work
aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome
these gaps. Two methods are reviewed and compared. An example database was designed from RANS k-
ε CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of
quality, real-time applicability and real-life plausibility.
RS 2022
Implicit material modelling using AI techniques
and big data generation
Rúben Lourenço
Supervisory team: A. Andrade-Campos, Pétia Georgieva
Doctoral Programme in Mechanical Engineering
TEMA – Centre for Mechanical Technology and Automation
Towards virtual forming and AI Implicit material modelling using AI technique...vformxsteels
RS 2022
Rúben Lourenço
Supervisory team: A. Andrade-Campos, Pétia Georgieva
Doctoral Programme in Mechanical Engineering
TEMA – Centre for Mechanical Technology and Automation
Parameter Identification of Swift law using a FEMU-based approach and an inno...vformxsteels
ESAFORM 2022
M. Conde1, J. Henriques1,
S. Coppieters2, A. Andrade-Campos1
1 TEMA, Department of Mechanical Engineering, University of Aveiro, Portugal
2 Department of Materials Engineering, Ghent Technology Campus, KU Leuven, Belgium
Parameter Identification of Swift law using a FEMU-based approach and an inno...vformxsteels
ESAFORM 2022
M. Conde1, J. Henriques1,
S. Coppieters2, A. Andrade-Campos1
1 TEMA, Department of Mechanical Engineering, University of Aveiro, Portugal
2 Department of Materials Engineering, Ghent Technology Campus, KU Leuven, Belgium
Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Att...ShuvamRoy12
Roy, A. and Bagade, P. (2024). Attentive-YOLO: On-Site Water Pipeline Inspection Using Efficient Channel Attention and Reduced ELAN-Based YOLOv7. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 492-499.
Assessment of likely consequences of a potential accident is a major concern for loss prevention and
safety promotion in process industry. Loss of confinement on a storage tank, vessel or piping on industrial
sites could imply atmospheric dispersion of toxic or flammable gases. Gas dispersion forecasting is a
difficult task since turbulence modeling at large scale involves expensive calculations. Therefore simpler
models are used but remain inaccurate especially when turbulence is heterogeneous. The present work
aims to study if Artificial Neural Networks coupled with Cellular Automata could be relevant to overcome
these gaps. Two methods are reviewed and compared. An example database was designed from RANS k-
ε CFD model. Both methods were then applied. Their efficiencies are compared and discussed in terms of
quality, real-time applicability and real-life plausibility.
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The desktop is projected
on a wall using a projector, which gives the user the free experience
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_________________________________________________________________
Windows Live™: Keep your life in sync. Check it out!
http://windowslive.com/explore?ocid=TXT_TAGLM_WL_t1_allup_explore_012009
It is actively developed by the Institute for Automation of Complex Power Systems.
Presented by Prof. Antonello Monti during ERIGrid - VILLAS workshop on 13th September 2018 at OFFIS, Oldenburg.
https://www.acs.eonerc.rwth-aachen.de
https://www.fein-aachen.org/projects/villas-framework/
Definition and Validation of Scientific Algorithms for the SEOSAT/Ingenio GPPEsri
Presentation by Eduardo de Miguel, Raúl Valenzuela, Teodoro Bernardino, Verena Rodríguez, Alberto Pizarro, Diana de Miguel and Severino Fernández from INTA, GMV and EADS-CASA made on Esri European User Conference 2011.
Automatic fabric defect detection employing deep learningIJECEIAES
A major issue for fabric quality inspection is in the detection of defaults, it has become an extremely challenging goal for the textile industry to minimize costs in both production and quality inspection. The quality inspection is currently done manually by professionals; hence the need for the implementation of a fast, powerful, robust, and intelligent machine vision system in order to achieve high global quality, uniformity, and consistency of fabrics and to increase productivity. Consequently, the automatic inspection control process can improve productivity and enhance product quality. This article describes the approach used in developing a convolutional neural network for identifying fabric defects from input images of fabric surfaces. The proposed neural network is a pre-trained convolutional model ‘DetectNet’, it was adapted to be more efficient to the fabric image feature extraction. The developed model is capable of successfully distinguishing between defective fabric and non-defective with 93% accuracy for the first model and 96% for the second model.
Online video-based abnormal detection using highly motion techniques and stat...TELKOMNIKA JOURNAL
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Identification of anisotropic yield functions using an information-rich tensi...vformxsteels
Yi Zhang1, António Andrade-Campos2, Sam Coppieters1
1 Department of Materials Engineering, KU Leuven
2 Department of Mechanical Engineering, University of Aveiro, 3810-193
Aveiro, Portugal
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The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
1. 25th International Conference on Material Forming
The Virtual Fields Method to indirectly train ANNs for
implicit constitutive modelling
ESAFORM 2022
R. Lourenço1,a, A. Andrade-Campos1,b, P. Georgieva2,c
1Dept. of Mechanical Engineering
Centre for Mechanical Technology and Automation, University of Aveiro, Portugal
2Dept. of Electronics Telecommunications and Informatics
Institute of Electronics and Informatics Engineering, University of Aveiro, Portugal
arubenl@ua.pt, bgilac@ua.pt, cpetia@ua.pt
27-29 April, Braga, Portugal
EUROPEAN UNION
European Social Fund
2. 25th International Conference on
Material Forming
Session outline
ESAFORM 2022
• Introduction
• Implicit constitutive modelling (direct and indirect training)
• Coupling the Virtual Fields Method with an ANN
• Numerical example
• Conclusions
1
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
3. Introduction
• Constitutive models rely on empirical parameters that need to be
calibrated
• Complex models with a higher number of parameters require
expensive and time-consuming experimental campaigns
• Artificial Neural Networks (ANNs) have the potential to provide a
radically different approach to the field
• ANNs can implicitly learn patterns directly from data, without assuming
a mathematical formulation or identifying parameters
25th International Conference on
Material Forming
ESAFORM 2022
2
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
4. Implicit constitutive modelling
Direct approach
• Most common approach in the literature
• Data is numerically generated
• Labelled data pairs (stress-strain)
• Easier to train with a ground-truth value
• Variable to predict not always obtainable in a real experimental setting
25th International Conference on
Material Forming
ESAFORM 2022
3
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
5. Implicit constitutive modelling
Indirect approach
• Not widely used
• Data numerically generated or from full-field measurements
• Relies only on directly measurable data (e.g. displacements, global force)
• Variable to predict is indirectly obtained from measurable or intermediate
variables
• Harder to train
25th International Conference on
Material Forming
ESAFORM 2022
4
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
6. Coupled FEM-ANN model1
1Liu, X, et al., Learning nonlinear constitutive laws using neural network models based on indirectly measurable data. J. Appl. Mech.,
Transactions ASME 2020, vol. 87, pp 1–8.
25th International Conference on
Material Forming
ESAFORM 2022
5
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
7. Coupled VFM-ANN model
25th International Conference on
Material Forming
ESAFORM 2022
6
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
8. Numerical example - heterogeneous test1
1J.M.P. Martins, A. Andrade-Campos, S. Thuillier. Comparison of inverse identification strategies for constitutive mechanical models
using full-field measurements, Int. J. Mech. Sci., vol. 145, pp 330–345, 2018
Swift’s hardening law
Surface traction distribution
Global force
25th International Conference on
Material Forming
ESAFORM 2022
7
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
9. • Numerically generated on Abaqus
• Mesh: 9x9 CPS4R elements
• 1000 time points
• Elastic and full-elastoplastic responses
Numerical example - data generation
Training set Validation set
25th International Conference on
Material Forming
ESAFORM 2022
8
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
10. Numerical example - ANN model
PReLU1
1. K. He, et al., Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet
Classification, 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015
• Adam optimizer
• LR scheduler (ReduceLROnPlateau)
• Early-stopping
• Epochs
• 20 epochs (elastic model)
• 77 epochs (elastoplastic model)
25th International Conference on
Material Forming
ESAFORM 2022
9
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
11. 25th International Conference on
Material Forming
ESAFORM 2022
10
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
12. Numerical example – learning curves
Elastic model Elastoplastic model
25th International Conference on
Material Forming
ESAFORM 2022
11
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
13. Numerical example – validation results
• Overall, good predictions for stress along 𝑥-direction
• Notably lower sensitivity to the stress responses along 𝑦 and 𝑥𝑦
25th International Conference on
Material Forming
ESAFORM 2022
12
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
14. Numerical example – virtual fields
* *
* *
25th International Conference on
Material Forming
ESAFORM 2022
13
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
* Virtual fields used to train the elastic model
15. Numerical example – validation results
• Improved shear stress prediction for the elastic response, slight
improvement in the elastoplastic response
• Low sensitivity to stress along 𝑦-direction
25th International Conference on
Material Forming
ESAFORM 2022
14
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
16. Conclusions
• A coupled VFM-ANN system was proposed for implicit constitutive modelling
• Only directly measurable data is used. FEM not required for further calculations
• Elastic and elastoplastic response models were trained for a given material using
manually-defined virtual fields
• In general, model predictions were good for stress along x-direction
• Poor stress predictions along 𝑦 and 𝑥𝑦 -directions possibly related to:
• manually-defined VFs – tied to the user experience
• unconstrained optimization – multiple possible solutions, some of them with
no physical meaning
• mechanical test - not rich enough
25th International Conference on
Material Forming
ESAFORM 2022
15
R. Lourenço, A. Andrade-Campos, P. Georgieva
The Virtual Fields Method to indirectly train ANNs for implicit constitutive modelling
17. Rúben Lourenço acknowledges the Portuguese Foundation for Science and Technology (FCT) for the financial support provided through the grant
2020.05279.BD, co-financed by the European Social Fund, through the Regional Operational Programme CENTRO 2020.
The authors also gratefully acknowledge the financial support of the Portuguese Foundation for Science and Technology (FCT) under the projects
CENTRO-01-0145-FEDER-029713, POCI-01-0145-FEDER-031243 and POCI-01-0145-FEDER-030592 by UE/FEDER through the programs CENTRO
2020 and COMPETE 2020, and UIDB/00481/2020 and UIDP/00481/2020-FCT under CENTRO-01-0145-FEDER-022083.
This project also supported by the Research Fund for Coal and Steel under grant agreement No 888153.
25th International Conference on Material Forming
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