https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Wed, 15 Apr 2026 22:45:24 GMT 2026-04-15T22:45:24Z http://hdl.handle.net/10985/15396 Tape surface characterization and classification in automated tape placement processability: Modeling and numerical analysis ARGERICH, Clara; IBÁÑEZ, Rubén; LEÓN, Angel; ABISSET-CHAVANNE, Emmanuelle; CHINESTA SORIA, Francisco Abstract: Many composite forming processes are based on the consolidation of preimpregnated preforms of different types, e.g., sheets, tapes, .... Composite plies are put in contact using different technologies and consolidation is performed by supplying heat and pressure, the first to promote molecular diffusion at the plies interface and both (heat and pressure) to facilitate the intimate contact by squeezing surface asperities. Optimal processing requires an intimate contact as large as possible between the surfaces put in contact, for different reasons: (i) first, a perfect contact becomes compulsory to make possible molecular diffusion at the interface level in order to ensure bulk properties at interfaces; (ii) second, imperfect contact conditions result in micro and meso pores located at the interface, weakening it from the mechanical point of view, where macro defects (cracks, plies delamination, etc.) are susceptible of appearing. As just indicated, the main process parameters are the applied heat and pressure, as well as the process time (associated with the laying head velocity). These parameters should be adjusted to ensure optimal consolidation, avoiding imperfect bonding or thermal degradation. However, experiments evidence that the consolidation degree is strongly dependent on the surface characteristics (roughness). The same process parameters applied to different surfaces produce very different degrees of intimate contact. The present study aims at identifying the main surface descriptors able to describe the evolution of the degree of intimate contact during processing. That knowledge is crucial for online process control in order to maximize both productivity and part quality. Mon, 01 Jan 2018 00:00:00 GMT http://hdl.handle.net/10985/15396 2018-01-01T00:00:00Z ARGERICH, Clara IBÁÑEZ, Rubén LEÓN, Angel ABISSET-CHAVANNE, Emmanuelle CHINESTA SORIA, Francisco Abstract: Many composite forming processes are based on the consolidation of preimpregnated preforms of different types, e.g., sheets, tapes, .... Composite plies are put in contact using different technologies and consolidation is performed by supplying heat and pressure, the first to promote molecular diffusion at the plies interface and both (heat and pressure) to facilitate the intimate contact by squeezing surface asperities. Optimal processing requires an intimate contact as large as possible between the surfaces put in contact, for different reasons: (i) first, a perfect contact becomes compulsory to make possible molecular diffusion at the interface level in order to ensure bulk properties at interfaces; (ii) second, imperfect contact conditions result in micro and meso pores located at the interface, weakening it from the mechanical point of view, where macro defects (cracks, plies delamination, etc.) are susceptible of appearing. As just indicated, the main process parameters are the applied heat and pressure, as well as the process time (associated with the laying head velocity). These parameters should be adjusted to ensure optimal consolidation, avoiding imperfect bonding or thermal degradation. However, experiments evidence that the consolidation degree is strongly dependent on the surface characteristics (roughness). The same process parameters applied to different surfaces produce very different degrees of intimate contact. The present study aims at identifying the main surface descriptors able to describe the evolution of the degree of intimate contact during processing. That knowledge is crucial for online process control in order to maximize both productivity and part quality. http://hdl.handle.net/10985/19161 Tape surfaces characterization with persistence images FRAHI, Tarek; ARGERICH, Clara; YUN, Minyoung; FALCO, Antonio; BARASINSKI, Anais; CHINESTA SORIA, Francisco The aim of this paper is to leverage the main surface topological descriptors to classify tape surface profiles, through the modelling of the evolution of the degree of intimate contact along the consolidation of pre-impregnated preforms associated to a composite forming process. It is well-known at an experimental level that the consolidation degree strongly depends on the surface characteristics (roughness). In particular, same process parameters applied to di erent surfaces produce very di erent degrees of intimate contact. It allows us to think that the surface topology plays an important role along this process. However, solving the physics-based models for simulating the roughness squeezing occurring at the tapes interface represents a computational e ort incompatible with online process control purposes. An alternative approach consists of taking a population of di erent tapes, with di erent surfaces, and simulating the consolidation for evaluating for each one the progression of the degree of intimate contact –DIC– while compressing the heated tapes, until reaching its final value at the end of the compression. The final goal is creating a regression able to assign a final value of the DIC to any surface, enabling online process control. The main issue of such an approach is the rough surface description, that is, the most precise and compact way of describing it from some appropriate parameters easy to extract experimentally, to be included in the just referred regression. In the present paper we consider a novel, powerful and very promising technique based on the topological data analysis –TDA– that considers an adequate metrics to describe, compare and classify rough surfaces. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/19161 2020-01-01T00:00:00Z FRAHI, Tarek ARGERICH, Clara YUN, Minyoung FALCO, Antonio BARASINSKI, Anais CHINESTA SORIA, Francisco The aim of this paper is to leverage the main surface topological descriptors to classify tape surface profiles, through the modelling of the evolution of the degree of intimate contact along the consolidation of pre-impregnated preforms associated to a composite forming process. It is well-known at an experimental level that the consolidation degree strongly depends on the surface characteristics (roughness). In particular, same process parameters applied to di erent surfaces produce very di erent degrees of intimate contact. It allows us to think that the surface topology plays an important role along this process. However, solving the physics-based models for simulating the roughness squeezing occurring at the tapes interface represents a computational e ort incompatible with online process control purposes. An alternative approach consists of taking a population of di erent tapes, with di erent surfaces, and simulating the consolidation for evaluating for each one the progression of the degree of intimate contact –DIC– while compressing the heated tapes, until reaching its final value at the end of the compression. The final goal is creating a regression able to assign a final value of the DIC to any surface, enabling online process control. The main issue of such an approach is the rough surface description, that is, the most precise and compact way of describing it from some appropriate parameters easy to extract experimentally, to be included in the just referred regression. In the present paper we consider a novel, powerful and very promising technique based on the topological data analysis –TDA– that considers an adequate metrics to describe, compare and classify rough surfaces. http://hdl.handle.net/10985/18955 Nonlinear Regression Operating on Microstructures Described from Topological Data Analysis for the Real-Time Prediction of Effective Properties YUN, Minyoung; ARGERICH, Clara; CUETO, Elias; DUVAL, Jean Louis; CHINESTA SORIA, Francisco Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/18955 2020-01-01T00:00:00Z YUN, Minyoung ARGERICH, Clara CUETO, Elias DUVAL, Jean Louis CHINESTA SORIA, Francisco Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making. http://hdl.handle.net/10985/19137 On the data-driven modeling of reactive extrusion IBAÑEZ, Ruben; CASTERAN, Fanny; ARGERICH, Clara; HASCOET, Nicolas; CASSAGNAU, Philippe; GHNATIOS, Chady; AMMAR, Amine; CHINESTA SORIA, Francisco This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/19137 2020-01-01T00:00:00Z IBAÑEZ, Ruben CASTERAN, Fanny ARGERICH, Clara HASCOET, Nicolas CASSAGNAU, Philippe GHNATIOS, Chady AMMAR, Amine CHINESTA SORIA, Francisco This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion. http://hdl.handle.net/10985/19991 Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation CASTÉRAN, Fanny; IBANEZ, Ruben; ARGERICH, Clara; DELAGE, Karim; CASSAGNAU, Philippe; CHINESTA SORIA, Francisco The purpose of this paper is to combine a classical 1D twin-screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin-screw extrusion are studied for this purpose. The twin-screw extrusion simulation software LUDOVIC is used and machine learning techniques dealing with low data limit are used as a correction of the simulation. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/19991 2020-01-01T00:00:00Z CASTÉRAN, Fanny IBANEZ, Ruben ARGERICH, Clara DELAGE, Karim CASSAGNAU, Philippe CHINESTA SORIA, Francisco The purpose of this paper is to combine a classical 1D twin-screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin-screw extrusion are studied for this purpose. The twin-screw extrusion simulation software LUDOVIC is used and machine learning techniques dealing with low data limit are used as a correction of the simulation.