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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 24 Feb 2024 11:16:24 GMT2024-02-24T11:16:24ZIJMF 10th anniversary - Advances in Material Forming
http://hdl.handle.net/10985/20600
IJMF 10th anniversary - Advances in Material Forming
CHINESTA SORIA, Francisco
No abstract
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/206002020-01-01T00:00:00ZCHINESTA SORIA, FranciscoNo abstractManifold learning for coherent design interpolation based on geometrical and topological descriptors
http://hdl.handle.net/10985/24632
Manifold learning for coherent design interpolation based on geometrical and topological descriptors
MUNOZ, David; ALLIX, Olivier; CHINESTA SORIA, Francisco; RÓDENAS, Juan José
In the context of intellectual property in the manufacturing industry, know-how is referred to practical knowledge on how to accomplish a specific task. This know-how is often difficult to be synthesised in a set of rules or steps as it remains in the intuition and expertise of engineers, designers, and other professionals. Today, a new research line in this concern spot-up thanks to the explosion of Artificial Intelligence and Machine Learning algorithms and its alliance with Computational Mechanics and Optimisation tools. However, a key aspect with industrial design is the scarcity of available data, making it problematic to rely on deep-learning approaches. Assuming that the existing designs live in a manifold, in this paper, we propose a synergistic use of existing Machine Learning tools to infer a reduced manifold from the existing limited set of designs and, then, to use it to interpolate between the individuals, working as a generator basis, to create new and coherent designs. For this, a key aspect is to be able to properly interpolate in the reduced manifold, which requires a proper clustering of the individuals. From our experience, due to the scarcity of data, adding topological descriptors to geometrical ones considerably improves the quality of the clustering. Thus, a distance, mixing topology and geometry is proposed. This distance is used both, for the clustering and for the interpolation. For the interpolation, relying on optimal transport appear to be mandatory. Examples of growing complexity are proposed to illustrate the goodness of the method.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/246322023-01-01T00:00:00ZMUNOZ, DavidALLIX, OlivierCHINESTA SORIA, FranciscoRÓDENAS, Juan JoséIn the context of intellectual property in the manufacturing industry, know-how is referred to practical knowledge on how to accomplish a specific task. This know-how is often difficult to be synthesised in a set of rules or steps as it remains in the intuition and expertise of engineers, designers, and other professionals. Today, a new research line in this concern spot-up thanks to the explosion of Artificial Intelligence and Machine Learning algorithms and its alliance with Computational Mechanics and Optimisation tools. However, a key aspect with industrial design is the scarcity of available data, making it problematic to rely on deep-learning approaches. Assuming that the existing designs live in a manifold, in this paper, we propose a synergistic use of existing Machine Learning tools to infer a reduced manifold from the existing limited set of designs and, then, to use it to interpolate between the individuals, working as a generator basis, to create new and coherent designs. For this, a key aspect is to be able to properly interpolate in the reduced manifold, which requires a proper clustering of the individuals. From our experience, due to the scarcity of data, adding topological descriptors to geometrical ones considerably improves the quality of the clustering. Thus, a distance, mixing topology and geometry is proposed. This distance is used both, for the clustering and for the interpolation. For the interpolation, relying on optimal transport appear to be mandatory. Examples of growing complexity are proposed to illustrate the goodness of the method.Parametric Analysis of Thick FGM Plates Based on 3D Thermo-Elasticity Theory: A Proper Generalized Decomposition Approach
http://hdl.handle.net/10985/24728
Parametric Analysis of Thick FGM Plates Based on 3D Thermo-Elasticity Theory: A Proper Generalized Decomposition Approach
KAZEMZADEH-PARSI, Mohammad-Javad; AMMAR, Amine; CHINESTA SORIA, Francisco
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Parametric Analysis of Thick FGM Plates Based on 3D Thermo-Elasticity Theory: A Proper Generalized Decomposition Approach
by Mohammad-Javad Kazemzadeh-Parsi
1,* [ORCID] , Amine Ammar
1 [ORCID] and Francisco Chinesta
2
1
LAMPA & ESI Group Chair, Arts et Metiers Institute of Technology, 49035 Angers, France
2
PIMM Lab & ESI Group Chair, Arts et Metiers Institute of Technology, 75013 Paris, France
*
Author to whom correspondence should be addressed.
Materials 2023, 16(4), 1753; https://doi.org/10.3390/ma16041753
Submission received: 19 December 2022 / Revised: 10 February 2023 / Accepted: 17 February 2023 / Published: 20 February 2023
(This article belongs to the Topic Artificial Intelligence and Computational Methods: Modeling, Simulations and Optimization of Complex Systems)
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Abstract
In the present work, the general and well-known model reduction technique, PGD (Proper Generalized Decomposition), is used for parametric analysis of thermo-elasticity of FGMs (Functionally Graded Materials). The FGMs have important applications in space technologies, especially when a part undergoes an extreme thermal environment. In the present work, material gradation is considered in one, two and three directions, and 3D heat transfer and theory of elasticity equations are solved to have an accurate temperature field and be able to consider all shear deformations. A parametric analysis of FGM materials is especially useful in material design and optimization. In the PGD technique, the field variables are separated to a set of univariate functions, and the high-dimensional governing equations reduce to a set of one-dimensional problems. Due to the curse of dimensionality, solving a high-dimensional parametric problem is considerably more computationally intensive than solving a set of one-dimensional problems. Therefore, the PGD makes it possible to handle high-dimensional problems efficiently. In the present work, some sample examples in 4D and 5D computational spaces are solved, and the results are presented.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/247282023-01-01T00:00:00ZKAZEMZADEH-PARSI, Mohammad-JavadAMMAR, AmineCHINESTA SORIA, Franciscofirst_page
settings
Order Article Reprints
Open AccessArticle
Parametric Analysis of Thick FGM Plates Based on 3D Thermo-Elasticity Theory: A Proper Generalized Decomposition Approach
by Mohammad-Javad Kazemzadeh-Parsi
1,* [ORCID] , Amine Ammar
1 [ORCID] and Francisco Chinesta
2
1
LAMPA & ESI Group Chair, Arts et Metiers Institute of Technology, 49035 Angers, France
2
PIMM Lab & ESI Group Chair, Arts et Metiers Institute of Technology, 75013 Paris, France
*
Author to whom correspondence should be addressed.
Materials 2023, 16(4), 1753; https://doi.org/10.3390/ma16041753
Submission received: 19 December 2022 / Revised: 10 February 2023 / Accepted: 17 February 2023 / Published: 20 February 2023
(This article belongs to the Topic Artificial Intelligence and Computational Methods: Modeling, Simulations and Optimization of Complex Systems)
Download
keyboard_arrow_down
Browse Figures
Versions Notes
Abstract
In the present work, the general and well-known model reduction technique, PGD (Proper Generalized Decomposition), is used for parametric analysis of thermo-elasticity of FGMs (Functionally Graded Materials). The FGMs have important applications in space technologies, especially when a part undergoes an extreme thermal environment. In the present work, material gradation is considered in one, two and three directions, and 3D heat transfer and theory of elasticity equations are solved to have an accurate temperature field and be able to consider all shear deformations. A parametric analysis of FGM materials is especially useful in material design and optimization. In the PGD technique, the field variables are separated to a set of univariate functions, and the high-dimensional governing equations reduce to a set of one-dimensional problems. Due to the curse of dimensionality, solving a high-dimensional parametric problem is considerably more computationally intensive than solving a set of one-dimensional problems. Therefore, the PGD makes it possible to handle high-dimensional problems efficiently. In the present work, some sample examples in 4D and 5D computational spaces are solved, and the results are presented.Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
http://hdl.handle.net/10985/24724
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
HERNÁNDEZ, Quercus; BADIAS, Alberto; CHINESTA SORIA, Francisco; CUETO, Elias
We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems. Predictions can be done, however, at the scale of the complete system. Examples are shown on the performance of the proposed technique.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/247242023-01-01T00:00:00ZHERNÁNDEZ, QuercusBADIAS, AlbertoCHINESTA SORIA, FranciscoCUETO, EliasWe develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems. Predictions can be done, however, at the scale of the complete system. Examples are shown on the performance of the proposed technique.A thermodynamics-informed active learning approach to perception and reasoning about fluids
http://hdl.handle.net/10985/24727
A thermodynamics-informed active learning approach to perception and reasoning about fluids
MOYA GARCÍA, Beatriz; BADIAS, Alberto; GONZALEZ, David; CHINESTA SORIA, Francisco; CUETO
Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences
play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts
of future situations. We propose a thermodynamics-informed active learning strategy for fluid perception and reasoning
from observations. As a model problem, we take the sloshing phenomena of different fluids contained in a glass. Starting
from full-field and high-resolution synthetic data for a particular fluid, we develop a method for the tracking (perception)
and simulation (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera. This
approach demonstrates the importance of physics and knowledge not only in data-driven (gray-box) modeling but also in
real-physics adaptation in low-data regimes and partial observations of the dynamics. The presented method is extensible to
other domains such as the development of cognitive digital twins able to learn from observation of phenomena for which they
have not been trained explicitly.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/247272023-01-01T00:00:00ZMOYA GARCÍA, BeatrizBADIAS, AlbertoGONZALEZ, DavidCHINESTA SORIA, FranciscoCUETOLearning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences
play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts
of future situations. We propose a thermodynamics-informed active learning strategy for fluid perception and reasoning
from observations. As a model problem, we take the sloshing phenomena of different fluids contained in a glass. Starting
from full-field and high-resolution synthetic data for a particular fluid, we develop a method for the tracking (perception)
and simulation (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera. This
approach demonstrates the importance of physics and knowledge not only in data-driven (gray-box) modeling but also in
real-physics adaptation in low-data regimes and partial observations of the dynamics. The presented method is extensible to
other domains such as the development of cognitive digital twins able to learn from observation of phenomena for which they
have not been trained explicitly.Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites
http://hdl.handle.net/10985/24737
Parametric Damage Mechanics Empowering Structural Health Monitoring of 3D Woven Composites
JACOT, Maurine; CHAMPANEY, Victor; CHINESTA SORIA, Francisco; CORTIAL, Julien
This paper presents a data-driven structural health monitoring (SHM) method by the use of so-called reduced-order models relying on an offline training/online use for unidirectional fiber and matrix failure detection in a 3D woven composite plate. During the offline phase (or learning) a dataset of possible damage localization, fiber and matrix failure ratios is generated through high-fidelity simulations (ABAQUS software). Then, a reduced model in a lower-dimensional approximation subspace based on the so-called sparse proper generalized decomposition (sPGD) is constructed. The parametrized approach of the sPGD method reduces the computational burden associated with a high-fidelity solver and allows a faster evaluation of all possible failure configurations. However, during the testing phase, it turns out that classical sPGD fails to capture the influence of the damage localization on the solution. To alleviate the just-referred difficulties, the present work proposes an adaptive sPGD. First, a change of variable is carried out to place all the damage areas on the same reference region, where an adapted interpolation can be done. During the online use, an optimization algorithm is employed with numerical experiments to evaluate the damage localization and damage ratio which allow us to define the health state of the structure.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/247372023-01-01T00:00:00ZJACOT, MaurineCHAMPANEY, VictorCHINESTA SORIA, FranciscoCORTIAL, JulienThis paper presents a data-driven structural health monitoring (SHM) method by the use of so-called reduced-order models relying on an offline training/online use for unidirectional fiber and matrix failure detection in a 3D woven composite plate. During the offline phase (or learning) a dataset of possible damage localization, fiber and matrix failure ratios is generated through high-fidelity simulations (ABAQUS software). Then, a reduced model in a lower-dimensional approximation subspace based on the so-called sparse proper generalized decomposition (sPGD) is constructed. The parametrized approach of the sPGD method reduces the computational burden associated with a high-fidelity solver and allows a faster evaluation of all possible failure configurations. However, during the testing phase, it turns out that classical sPGD fails to capture the influence of the damage localization on the solution. To alleviate the just-referred difficulties, the present work proposes an adaptive sPGD. First, a change of variable is carried out to place all the damage areas on the same reference region, where an adapted interpolation can be done. During the online use, an optimization algorithm is employed with numerical experiments to evaluate the damage localization and damage ratio which allow us to define the health state of the structure.Thermodynamics-informed neural networks for physically realistic mixed reality
http://hdl.handle.net/10985/24750
Thermodynamics-informed neural networks for physically realistic mixed reality
HERNÁNDEZ, Quercus; BADIAS, Alberto; CHINESTA SORIA, Francisco; CUETO, Elias
The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience. Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to prove the performance of the method.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/247502023-01-01T00:00:00ZHERNÁNDEZ, QuercusBADIAS, AlbertoCHINESTA SORIA, FranciscoCUETO, EliasThe imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience. Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to prove the performance of the method.Physics Perception in Sloshing Scenes With Guaranteed Thermodynamic Consistency
http://hdl.handle.net/10985/24796
Physics Perception in Sloshing Scenes With Guaranteed Thermodynamic Consistency
MOYA, Beatriz; BADIAS, Alberto; GONZALEZ, David; CHINESTA SORIA, Francisco; CUETO, Elias
Physics perception very often faces the problem that only limited data or partial measurements on the scene are available. In this work, we propose a strategy to learn the full state of sloshing liquids from measurements of the free surface. Our approach is based on recurrent neural networks (RNN) that project the limited information available to a reduced-order manifold to not only reconstruct the unknown information but also be capable of performing fluid reasoning about future scenarios in real-time. To obtain physically consistent predictions, we train deep neural networks on the reduced-order manifold that, through the employ of inductive biases, ensure the fulfillment of the principles of thermodynamics. RNNs learn from history the required hidden information to correlate the limited information with the latent space where the simulation occurs. Finally, a decoder returns data to the high-dimensional manifold, to provide the user with insightful information in the form of augmented reality. This algorithm is connected to a computer vision system to test the performance of the proposed methodology with real information, resulting in a system capable of understanding and predicting future states of the observed fluid in real-time.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/247962023-01-01T00:00:00ZMOYA, BeatrizBADIAS, AlbertoGONZALEZ, DavidCHINESTA SORIA, FranciscoCUETO, EliasPhysics perception very often faces the problem that only limited data or partial measurements on the scene are available. In this work, we propose a strategy to learn the full state of sloshing liquids from measurements of the free surface. Our approach is based on recurrent neural networks (RNN) that project the limited information available to a reduced-order manifold to not only reconstruct the unknown information but also be capable of performing fluid reasoning about future scenarios in real-time. To obtain physically consistent predictions, we train deep neural networks on the reduced-order manifold that, through the employ of inductive biases, ensure the fulfillment of the principles of thermodynamics. RNNs learn from history the required hidden information to correlate the limited information with the latent space where the simulation occurs. Finally, a decoder returns data to the high-dimensional manifold, to provide the user with insightful information in the form of augmented reality. This algorithm is connected to a computer vision system to test the performance of the proposed methodology with real information, resulting in a system capable of understanding and predicting future states of the observed fluid in real-time.Describing and Modeling Rough Composites Surfaces by Using Topological Data Analysis and Fractional Brownian Motion
http://hdl.handle.net/10985/24797
Describing and Modeling Rough Composites Surfaces by Using Topological Data Analysis and Fractional Brownian Motion
RUNACHER, Antoine; KAZEMZADEH-PARSI, Mohammad-Javad; DI LORENZO, Daniele; CHAMPANEY, Victor; HASCOET, Nicolas; AMMAR, Amine; CHINESTA SORIA, Francisco
Many composite manufacturing processes employ the consolidation of pre-impregnated preforms. However, in order to obtain adequate performance of the formed part, intimate contact and molecular diffusion across the different composites’ preform layers must be ensured. The latter takes place as soon as the intimate contact occurs and the temperature remains high enough during the molecular reptation characteristic time. The former, in turn, depends on the applied compression force, the temperature and the composite rheology, which, during the processing, induce the flow of asperities, promoting the intimate contact. Thus, the initial roughness and its evolution during the process, become critical factors in the composite consolidation. Processing optimization and control are needed for an adequate model, enabling it to infer the consolidation degree from the material and process features. The parameters associated with the process are easily identifiable and measurable (e.g., temperature, compression force, process time, ⋯). The ones concerning the materials are also accessible; however, describing the surface roughness remains an issue. Usual statistical descriptors are too poor and, moreover, they are too far from the involved physics. The present paper focuses on the use of advanced descriptors out-performing usual statistical descriptors, in particular those based on the use of homology persistence (at the heart of the so-called topological data analysis—TDA), and their connection with fractional Brownian surfaces. The latter constitutes a performance surface generator able to represent the surface evolution all along the consolidation process, as the present paper emphasizes.
Sun, 01 Jan 2023 00:00:00 GMThttp://hdl.handle.net/10985/247972023-01-01T00:00:00ZRUNACHER, AntoineKAZEMZADEH-PARSI, Mohammad-JavadDI LORENZO, DanieleCHAMPANEY, VictorHASCOET, NicolasAMMAR, AmineCHINESTA SORIA, FranciscoMany composite manufacturing processes employ the consolidation of pre-impregnated preforms. However, in order to obtain adequate performance of the formed part, intimate contact and molecular diffusion across the different composites’ preform layers must be ensured. The latter takes place as soon as the intimate contact occurs and the temperature remains high enough during the molecular reptation characteristic time. The former, in turn, depends on the applied compression force, the temperature and the composite rheology, which, during the processing, induce the flow of asperities, promoting the intimate contact. Thus, the initial roughness and its evolution during the process, become critical factors in the composite consolidation. Processing optimization and control are needed for an adequate model, enabling it to infer the consolidation degree from the material and process features. The parameters associated with the process are easily identifiable and measurable (e.g., temperature, compression force, process time, ⋯). The ones concerning the materials are also accessible; however, describing the surface roughness remains an issue. Usual statistical descriptors are too poor and, moreover, they are too far from the involved physics. The present paper focuses on the use of advanced descriptors out-performing usual statistical descriptors, in particular those based on the use of homology persistence (at the heart of the so-called topological data analysis—TDA), and their connection with fractional Brownian surfaces. The latter constitutes a performance surface generator able to represent the surface evolution all along the consolidation process, as the present paper emphasizes.