Academic literature on the topic 'Advanced Composite Processing (simplified CRTM)'

This study evaluates multimodal imaging for characterizing microstructures in partially impregnated thermoplastic matrix composites made of woven glass fiber and polypropylene. The research quantifies the impregnation degree of fiber bundles within composite plates manufactured through a simplified compression resin transfer molding process. For comparison, a reference plate was produced using compression molding of film stacks. An original surface polishing procedure was introduced to minimize surface defects while polishing partially impregnated samples. Extended-field 2D imaging techniques, including polarized light, fluorescence, and scanning electron microscopies, were used to generate images of the same microstructure at fiber-scale resolutions throughout the plate. Post-processing workflows at the macro-scale involved stitching, rigid registration, and pixel classification of FM and SEM images. Meso-scale workflows focused on 0°-oriented fiber bundles extracted from extended-field images to conduct quantitative analyses of glass fiber and porosity area fractions. A one-way ANOVA analysis confirmed the reliability of the statistical data within the 95% confidence interval. Porosity quantification based on the conducted multimodal approach indicated the sensitivity of the impregnation degree according to the layer distance from the pool of melted polypropylene in the context of simplified-CRTM. The findings underscore the potential of multimodal imaging for quantitative analysis in composite material production.

The presence of inhomogeneity in the porous electrodes of lithium-ion cells can significantly affect the active material utilization, energy/power density, and ageing dynamics of the cell, among others. 1-4 In Li-ion cells, inhomogeneity is caused by an uneven spatial distribution of the active material, carbon, binder, and electrolyte, or porous electrode micro/nanoscale properties such as the contact resistances, porosity and tortuosity. The existing literature suggests that the heterogeneity could inadvertently be introduced to the electrode with improper combination of design parameters and manufacturing steps, e.g. slurry formulation, thickness, mixing, and drying.5 Important insights into the local microstructure of the porous electrodes have been provided by FIB-SEM,6 X-ray tomography,7 or full simulation of electrode microstructure.8 Little attention has been paid, however, to develop formalisms to quantify the electrode heterogeneity and its correlation to the design parameters, manufacturing steps, and the battery performance. In this presentation, the electrochemical and microstructural characteristics of a large group of NMC111 and NMC622 porous electrodes with different design parameters will be presented and discussed. The experimental data were obtained by preparing porous electrodes of different thickness, porosity, formulation (NMC loading and carbon/binder ratio), and surface chemistry. We spotlight the sensitivity of the energy and power density of the Li|NMC cells to the microstructural indexes including tortuosity, porosity, and effective conductivities. The experimental data were analysed with the help of modeling and simulations to introduce a simplified formalism which expresses the concept of heterogeneity in the porous electrodes of lithium-ion batteries. References H. Hamed, S. Yari, J. D'Haen, FU. Renner, N. Reddy, A. Hardy, M. Safari, “Demystifying Charge Transport Limitations in the Porous Electrodes of Lithium‐Ion Batteries,” Advanced Energy Materials (2020)10 (47), 2002492. S. Yari, H. Hamed, J. D’Haen, MK. Van Bael, FU. Renner, A. Hardy, M. Safari, “Constructive versus Destructive Heterogeneity in Porous Electrodes of Lithium-Ion Batteries,” ACS Applied Energy Materials (2020) 3 (12), 11820-11829. J. Harris and P. Lu, “Effects of Inhomogeneities—nanoscale to mesoscale—on the durability of Li-Ion batteries,” J. Phys. Chem. C, (2013) 117(13). M. Forouzan, B. A. Mazzeo, and D. R. Wheeler, “modeling the effects of electrode microstructural heterogeneities on Li-Ion battery performance and lifetime,” J. Electrochem. Soc., (2018) 165(10). Jaiser, N. Sanchez Salach, M. Baunach, P. Scharfer, and W. Schabel, “Impact of drying conditions and wet film properties on adhesion and film solidification of lithium-ion battery anodes,” Dry. Technol., (2017) 35(15). Kehrwald, P. R. Shearing, N. P. Brandon, P. K. Sinha, and S. J. Harris, “local tortuosity inhomogeneities in a lithium battery composite electrode,” J. Electrochem. Soc., (2011) 158(12). Ebner, D. W. Chung, R. E. García, and V. Wood, “tortuosity anisotropy in lithium-ion battery electrodes,” Adv. Energy Mater., (2014) 4(5). Liu, V. Battaglia, and P. P. Mukherjee, “mesoscale elucidation of the influence of mixing sequence in electrode processing,” Langmuir, (2014) 30(50).

This article embarks on an insightful journey through the realm of advanced data analysis techniques which can be used in the insurance area, with a keen focus on the applications and capabilities of Graph Neural Networks (GNN) in the following sector. The article is structured into several chapters, which include the overview of existing and commonly used approaches of the data representation, the possible ways of data analysis of the data in such a representation, deep dive into the concept of GNN for the graph data analysis and the applicability of each approach in the insurance industry. The initial chapter introduces the two main concepts of the data representation, which are the commonly used relational database and the more modern approach of dimensional data design. Then the focus is moved to the graph data representation, which also can be used for data analysis in the cloud environment. To achieve the best applicability in the insurance industry, particularly in underwriting and claims management, the article analyzes the advantages of each approach to the data representation as well as its drawbacks. To conclude the chapter, the comparison table of the three approaches is presented. Based on the comparison table, the decision to use the graph representation is made as it enables the industry to unravel complex relationships and dependencies amid various data points—such as policyholder history, incident particulars, and third-party information—resulting in more accurate risk assessments and efficient claim resolutions. Then the article presents the concept of Graph Neural Networks, a rather new concept which can be used to analyze the data, represented in a graph form using machine learning algorithms. The potential of using this approach for the data analysis in the insurance area and some possible use cases are described. The advantages of using this approach include ability to effectively capture and leverage the complex relationships inherent in graph- structured data and a powerful framework for analyzing and processing graph-structured data. However, the potential drawbacks of the approach such as complexity to design and difficulties in scaling are also considered. Further along, the article probes the strategic integration of Graph Neural Networks with real-time and dynamic data environments, examining their adaptability to evolving network patterns and temporal dependencies. We discuss how this adaptability is paramount in contexts like real-time decision-making and predictive analysis, which are crucial for staying agile in a rapidly changing market landscape. Then the exact use cases of the GNN applicability in the insurance area are provided, including the claim assignment and underwriting process are described in detail. Furthermore, the simplified mathematical formulation of the underwriting process is provided, which elaborates the role GNNs play in propelling actuarial science with their capability to incorporate node attributes, edge information, and graph structure into a composite risk assessment algorithm. The article concludes by describing that with the new technologies, the graph representation may become the new standard for the data analysis in the cloud environment, especially for the insurance area, stressing the pivotal role of GNNs in navigating the complexities of interconnected, dynamic data and advocating for their continued research and development to unlock even greater potentials across various sectors.

AbstractThe dynamic scheduling problem of semiconductor manufacturing systems (SMSs) is becoming more complicated and challenging due to internal uncertainties and external demand changes. To this end, this paper addresses integrated release control and production scheduling problems with uncertain processing times and urgent orders and proposes a convolutional neural network and asynchronous advanced actor critic-based method (CNN-A3C) that involves a training phase and a deployment phase. In the training phase, actor–critic networks are trained to predict the evaluation of scheduling decisions and to output the optimal scheduling decision. In the deployment phase, the most appropriate release control and scheduling decisions are periodically generated according to the current production status based on the networks. Furthermore, we improve the four key points in the deep reinforcement learning (DRL) algorithm, state space, action space, reward function, and network structure and design four mechanisms: a slide-window-based two-dimensional state perception mechanism, an adaptive reward function that considers multiple objectives and automatically adjusts to dynamic events, a continuous action space based on composite dispatching rules (CDR) and release strategies, and actor–critic networks based on convolutional neural networks (CNNs). To verify the feasibility and effectiveness of the proposed dynamic scheduling method, it is implemented on a simplified SMS. The simulation experimental results show that the proposed method outperforms the unimproved A3C-based method and the common dispatching rules under the new uncertain scenarios.

Cette thèse présente une étude expérimentale pour améliorer le moulage par compression et transfert de résine thermoplastique (CRTM), axée sur l'efficacité industrielle, la durabilité et la recyclabilité, conformément aux objectifs de développement durable pour l’industrie, l’innovation et l’action climatique. En abordant la complexité de l'écoulement de la résine à plusieurs échelles dans le CRTM, cette recherche étudie l'écoulement transversal (à travers l’épaisseur) et la porosité induite par le processus à l'échelle méso des faisceaux de fibres de verre afin d'améliorer l'uniformité de l'imprégnation et le contrôle du compactage, en faisant le lien entre les cadres théoriques et les applications évolutives. L’étude est conduite sur une préforme, constituées de 6 couches de fibres de verre UD ([0/90]3) et d’une matrice thermoplastique en polypropylene (PP) mise en forme par un procédé CRTM . Un procédé « CRTM simplifié » permettant de contrôler la direction du front de matière est développé sur une presse industrielle, pilotée en déplacement. Trois configurations de procédé sont analysées : Configuration 1 (Référence) : configuration de type « film stacking » comme base de comparaison de la distribution de la résine et de la structure des fibres. Configuration 2 (CRTM simplifié) : Compression contrôlée par déplacement, les films de polymères formant initialement une couche unique en surface de la préforme. Configuration 3 (CRTM simplifié avec scellement des bords) : Compression améliorée avec un dispositif d’étanchéité limitant les fuites de résine en périphérie de la préforme et assurant un écoulement transversal. Un protocole d’analyse d'imagerie 2D est proposé, incluant l’analyse en lumière polarisée, la microscopie à fluorescence et la microscopie électronique à balayage pour caractériser qualitativement et quantitativement les taux de porosités au niveau des mèches et des plis de tissus. Un processus original de polissage en deux étapes permet de préserver l'intégrité de la surface. L'étude est complétée par une évaluation fine des mécanismes d'imprégnation à l'aide de la technique d'inspection hélicoïdale en microtomographie à rayon-X (micro-CT). Les résultats démontrent que les paramètres de compaction influencent directement le niveau d'imprégnation, atteignant une limite d'imprégnation. Cette thèse établit une démarche d’analyse du procédé CRTM pour des composites thermoplastiques haute performance, en vue d’une maitrise et d’une optimisation du procédé. Elle offre des perspectives sur des protocoles d’analyse précis basés sur l’étude à différentes échelles, améliorant la compréhension de l'interaction entre l'imprégnation et la perméabilité. Ces résultats répondent aux exigences de précision dans des secteurs tels que l'automobile et l'aérospatiale, où les composites CRTM sont essentiels pour les applications structurelles
This thesis presents an experimental study to advance thermoplastic Compression Resin Transfer Molding (CRTM), focusing on industrial efficiency, sustainability, and recyclability goals aligned with the Sustainable Development Goals for Industry, Innovation, and Climate Action. By addressing multi-scale resin flow complexity in CRTM, this research investigates transverse flow and process-induced porosity at the meso scale of glass fiber bundles to improve impregnation uniformity and compaction control, bridging theoretical frameworks with scalable applications. The study focuses on a thermoplastic polypropylene matrix reinforced with six layers of bidirectional UD woven glass fibers ([0/90]3) consolidated on a CRTM setup. The “Simplified CRTM” method is developed on an industrial press, using displacement-controlled compaction ratios. This method omits active resin injection, relying on a uniformly distributed viscous polymer pool beneath the unsaturated preform to drive resin flow uniformly with a unidirectional flow path. Controlled displacement and pressure optimize resin paths, manage fiber volume fraction, and reduce porosity. Three multi-step compaction configurations are evaluated: Configuration 1 (Reference): Uses force compaction as a baseline for comparing resin distribution and fiber structure. Configuration 2 (simplified CRTM): Displacement-controlled compaction enhances resin infiltration but faces challenges like edge race-tracking and fiber volume fraction (Vf) variability, affecting impregnation. Configuration 3 (simplified CRTM with Edge Sealing): Introduces high-temperature sealant tape at mold edges, limiting resin escape, maintaining transverse flow, and reducing porosity and race-tracking. Configuration 3 edge-sealing technique establishes a reproducible process for high quality CRTM composites. An advanced 2D multi-modal imaging protocol, tailored for partially impregnated samples produced via simplified CRTM with unfilled spaces and fragile microstructures, includes polarized light microscopy, fluorescence microscopy, and scanning electron microscopy for qualitative and quantitative characterization. An original two-step polishing process preserves surface integrity, and image post-processing workflows quantify impregnation quality and void distribution. The study is completed with a fine evaluation of the impregnation mechanisms using X-ray micro computed tomography technique (micro-CT) relying on helicoidal inspection method. Results demonstrate that compaction parameters directly impact impregnation level, reaching an impregnation limit. This thesis establishes a scalable, data-driven CRTM framework bridging laboratory experimentation with industrial requirements for high-performance thermoplastic composites. It offers insights into streamlined protocols and microstructure-based analysis, enhancing understanding of the interplay between impregnation and permeability in CRTM. These findings align with precision demands in sectors like automotive and aerospace, where CRTM composites are crucial for structural applications

There is a need for theoretical and computational tools that provide macroscopic relations for a composite continuum, starting from a description of the composite microstructure. The outlook for this viewpoint is particularly bright, given current trends in high-performance parallel supercomputing. This book is a step along those directions, with a special emphasis on a collection of mathematical methods that together build a base for advanced computational models. Consider the important example of the effective bulk properties of fiberreinforced materials consisting of fibers of minute cross section imbedded in a soft elastic epoxy. The physical properties of such materials is determined by the microstructure parameters: volume fraction occupied by the fibers versus continuous matrix; fiber orientations; shape of the fiber cross sections; and the spatial distribution of fibers. Hashin notes that “While for conventional engineering materials, such as metals and plastics, physical properties are almost exclusively determined by experiment, such an approach is impractical for FRM (fiber-reinforced materials) because of their great structural and physical variety,” The analysis of warpage and shrinkage of reinforced thermoset plastic parts provides yet another example of the important role played by computational models. The inevitable deformation of the fabricated part is influenced by the interplay between constituent material properties, the composite microstructure and macroscopic shape of the component. Computational models play an important role in controlling these deformations to minimize undesired directions that lead to warpage and shrinkage. The strength, stiffness, and low weight of these materials all result from the combination of a dispersed inclusion of very high modulus imbedded in a relatively soft and workable elastic matrix. It thus appears reasonable, as a first approximation, to consider a theory for the distribution of rigid (infinite modulus) inclusions in an elastic matrix, reserving the bulk of our efforts for the study of the role of inclusion microstructure. A framework for computational modeling has been established for materials processing, using models of microstructure with simplified rules for the motion of the inclusions.

Abstract Graphene-reinforced epoxy materials have become popular in engineering applications because of their advanced mechanical properties, such as their light weight and high strength. When introducing composite materials into structures, it has also brought great interest in how to monitor the functionality of the materials and identify material failure in real-time. Therefore, the structure-health monitoring of such materials has received great attention. Engineers are especially working on understanding the structure of composite materials and whether the mechanical properties and failure behavior could change when the contents vary in the composite materials. There is a broad range of non-destructive testing methods, such as ultrasonic and thermographic tests. In our study, we have chosen the acoustic emission (AE) method because it could be conducted with a minimal number of sensors and provide real-time feedback with a limited signal processing time. The goal of the proposed project was to validate the efficiency of using the AE method to identify the location of failure(s) in the graphene-reinforced structure. Sound source localization algorithms have been widely used with AE technology to identify the location of defects and cracks. It usually collects the time of arrival (TOA) or time differences of arrival (TDOA) at the sensor arrays, and calculates the location of the sound source, saying where the AE is generated by cracks, with the geometry information of the structure. In this project, three different types of samples were prepared, on epoxy with 1%, 2%, and 3% graphene-reinforced. The samples were shaped based on the ASME tensile test specimen standard. During the experiments, two sensors were placed on both ends of a sample, and a pencil lead break was used to produce AE signals at multiple locations in between the sensors on the sample. After each pencil lead break, the time instances of the first hit signal at each sensor were recorded. The TDOA between the two sensors was recorded. Because of the geometric setup of the samples, we simplified it to a two-dimensional problem. With the prior information of the break location measured, the TDOAs were used to identify the speed of sound traveling in the sample. Ten trials at multiple pencil breakpoints were conducted and the accuracy of AE source localization was evaluated. The mean and standard deviation values were plotted. The results of different samples with 1%, 2%, and 3% graphene content were compared. The future work of the project will be to conduct tensile tests on the samples and evaluate the AE localization effectiveness in monitoring fracture location in real-time.