https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Thu, 18 Jun 2026 04:17:47 GMT 2026-06-18T04:17:47Z http://hdl.handle.net/10985/15775 Multi-scale modeling and simulation of thermoplastic automated tape placement: Effects of metallic particles reinforcement on part consolidation LEÓN, Angel; PEREZ, Marta; BARASINSKI, Anaïs; DEFOORT, Brigitte; ABISSET-CHAVANNE, Emmanuelle; CHINESTA SORIA, Francisco This paper concerns engineered composites integrating metallic particles to enhance thermal and electrical properties. However, these properties are strongly dependent on the forming process itself that determines the particle distribution and orientation. At the same time, the resulting enhanced thermal properties affect the reinforced resin viscosity whose flow is involved in the intimate contact evolution. Thus, a subtle and intricate coupling appears, and the process cannot be defined by ignoring it. In this paper, we analyze the effects of particle concentration and orientation on the process and processability. For this purpose, three main models are combined: (i) a multi-scale surface representation and its evolution, by using an appropriate numerical model; (ii) flow-induced orientation, and (iii) the impact of the orientation state on the homogenized thermal conductivity. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10985/15775 2019-01-01T00:00:00Z LEÓN, Angel PEREZ, Marta BARASINSKI, Anaïs DEFOORT, Brigitte ABISSET-CHAVANNE, Emmanuelle CHINESTA SORIA, Francisco This paper concerns engineered composites integrating metallic particles to enhance thermal and electrical properties. However, these properties are strongly dependent on the forming process itself that determines the particle distribution and orientation. At the same time, the resulting enhanced thermal properties affect the reinforced resin viscosity whose flow is involved in the intimate contact evolution. Thus, a subtle and intricate coupling appears, and the process cannot be defined by ignoring it. In this paper, we analyze the effects of particle concentration and orientation on the process and processability. For this purpose, three main models are combined: (i) a multi-scale surface representation and its evolution, by using an appropriate numerical model; (ii) flow-induced orientation, and (iii) the impact of the orientation state on the homogenized thermal conductivity. http://hdl.handle.net/10985/15417 Sensitivity thermal analysis in the laser-assisted tape placement process PEREZ, Marta; BARASINSKI, Anaïs; COURTEMANCHE, Benoît; GHNATIOS, Chady; CHINESTA SORIA, Francisco Nowadays, the production of large pieces made of thermoplastic composites is an industrial challenging issue as there are yet several difficulties associated to their processing. The laserassisted tape placement (LATP) process is an automated manufacturing technique to produce long-fiber reinforced thermoplastic matrix composites. In this process, a tape is placed and progressively welded on the substrate. The main aim of the present work is to solve an almost state of the art thermal model by using an efficient numerical technique, the so-called Proper Generalized Decomposition (PGD) that considers parameters (geometrical and material) as model extra-coordinates. Within the PGD rationale the parametric temperature field is expressed in a separated form, as a finite sum of functional products, where each term depends on a single coordinate (space, time or each one of the parameters considered as extra-coordinates). Such a separated representation allows the explicit expression of the sensitivity fields, from the temperature derivative with respect to each parameter. These sensitivity fields represent a very valuable methodology to analyze and establish the influence of the critical input parameters on the thermal response, and therefore, for performing process optimization and control, as well as for evaluating the effect of parameters variability on the thermal response. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/15417 2018-01-01T00:00:00Z PEREZ, Marta BARASINSKI, Anaïs COURTEMANCHE, Benoît GHNATIOS, Chady CHINESTA SORIA, Francisco Nowadays, the production of large pieces made of thermoplastic composites is an industrial challenging issue as there are yet several difficulties associated to their processing. The laserassisted tape placement (LATP) process is an automated manufacturing technique to produce long-fiber reinforced thermoplastic matrix composites. In this process, a tape is placed and progressively welded on the substrate. The main aim of the present work is to solve an almost state of the art thermal model by using an efficient numerical technique, the so-called Proper Generalized Decomposition (PGD) that considers parameters (geometrical and material) as model extra-coordinates. Within the PGD rationale the parametric temperature field is expressed in a separated form, as a finite sum of functional products, where each term depends on a single coordinate (space, time or each one of the parameters considered as extra-coordinates). Such a separated representation allows the explicit expression of the sensitivity fields, from the temperature derivative with respect to each parameter. These sensitivity fields represent a very valuable methodology to analyze and establish the influence of the critical input parameters on the thermal response, and therefore, for performing process optimization and control, as well as for evaluating the effect of parameters variability on the thermal response. http://hdl.handle.net/10985/18459 Effects of material and process parameters on in-situ consolidation LEÓN, Angel; ARGERICH MARTÍN, Clara; BARASINSKI, Anaïs; SOCCARD, Eric; CHINESTA SORIA, Francisco Automated tape placement - ATP - is a recent manufacturing technology for composite materials. Therefore, a correct modeling of the multi-physical process is critical in order to make possible in-situ consolidation. In this work, we propose an accurate modelling framework and an efficient simulation procedure of physics occurring during the process with the aim of studying the influence of material and process parameters into the material consolidation evolution. For that purpose, an accurate description of the prepreg surface becomes compulsory, justifying the use of a multi-resolution description of it based on the use of wavelets. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10985/18459 2019-01-01T00:00:00Z LEÓN, Angel ARGERICH MARTÍN, Clara BARASINSKI, Anaïs SOCCARD, Eric CHINESTA SORIA, Francisco Automated tape placement - ATP - is a recent manufacturing technology for composite materials. Therefore, a correct modeling of the multi-physical process is critical in order to make possible in-situ consolidation. In this work, we propose an accurate modelling framework and an efficient simulation procedure of physics occurring during the process with the aim of studying the influence of material and process parameters into the material consolidation evolution. For that purpose, an accurate description of the prepreg surface becomes compulsory, justifying the use of a multi-resolution description of it based on the use of wavelets. http://hdl.handle.net/10985/18405 Code2vect: An efficient heterogenous data classifier and nonlinear regression technique ARGERICH MARTÍN, Clara; IBÁÑEZ PINILLO, Rubén; BARASINSKI, Anaïs; CHINESTA SORIA, Francisco The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm. Tue, 01 Jan 2019 00:00:00 GMT http://hdl.handle.net/10985/18405 2019-01-01T00:00:00Z ARGERICH MARTÍN, Clara IBÁÑEZ PINILLO, Rubén BARASINSKI, Anaïs CHINESTA SORIA, Francisco The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm. http://hdl.handle.net/10985/24747 Editorial: Advanced materials modeling combining model order reduction and data science GHNATIOS, Chady; BARASINSKI, Anaïs; CUETO, Elias The problem of efficiently building digital twins in the framework of materials manufacturing processes and materials modeling is thus, nowadays, a topic of increasing interest. Recent advances in digital twin technologies use experimental results to correct the simulation, but also to include their variability in the running simulation when a ground truth cannot be defined experimentally. This Research Topic addresses the recent developments in model reduction techniques, data-driven modeling, and digital twins technologies along with their applications in materials modeling and materials forming processes. Sun, 01 Jan 2023 00:00:00 GMT http://hdl.handle.net/10985/24747 2023-01-01T00:00:00Z GHNATIOS, Chady BARASINSKI, Anaïs CUETO, Elias The problem of efficiently building digital twins in the framework of materials manufacturing processes and materials modeling is thus, nowadays, a topic of increasing interest. Recent advances in digital twin technologies use experimental results to correct the simulation, but also to include their variability in the running simulation when a ground truth cannot be defined experimentally. This Research Topic addresses the recent developments in model reduction techniques, data-driven modeling, and digital twins technologies along with their applications in materials modeling and materials forming processes.