Papers by Keyword: Composite

Abstract: Direct compounding of long fiber thermoplastic (LFT-D) materials in compression molding are two complex processes in series linked by the plastificate. Continuous compounding and sequential compression create a time-dependent property progression along the extrusion direction of the plastificate. Under variation of secondary parameters, extruder die temperature, and die height of the LFT-D line, samples of plastificates, flow fronts and plates are manufactured and characterized. The plastificate density progression along the extrusion direction is primarily influenced by the temperature of the die. Lofting of the plastificate is higher at high temperatures while the density difference along the extrusion direction is lower. This density difference is known to influence fiber orientations and mechanical properties. The flow front of the material filling the mold is skewed because of the density difference. We show that the skewness is mainly influenced by the die height and is lower at high die heights. The fiber content distribution in the plate is discussed and found to be influenced by the length of the plastificate which is in turn determined by the secondary parameters. These secondary parameters of the LFT-D line can play a role in process optimization once the primary parameters are selected. This work provides clues and observations of principles for such optimizations.

Abstract: Thermoforming of thermoplastic fiber-reinforced composites enables cost-effective production of complex, high-volume components, yet wrinkling and shear-induced thickness variations remain persistent challenges in compound-curvature geometries, often leading to nonuniform consolidation. This work presents a predictive virtual process simulation that integrates discrete mesoscopic finite element modeling with targeted blank design strategies to address these limitations. The approach, developed by the Sherwood Group and implemented in LS-DYNA, is applied to the thermoforming of a UHMWPE unidirectional cross-ply composite system (DSM® Dyneema® HB210). A thickness-stretch shell formulation (SHELL ELFORM25), coupled with a user-defined material model, is employed to simultaneously capture in-plane shear, through-thickness deformation, and frictional interactions during forming. A parametric study is conducted to evaluate the combined effects of tooling geometry and strategically introduced slits in the blank, including side-and corner-oriented configurations. The results demonstrate that the proposed formulation provides an effective balance between computational efficiency and predictive accuracy while explicitly reducing shear-induced thickening. Notably, corner-oriented slits at 45° to the fiber directions significantly reduced thickness variability and wrinkle severity compared to unmodified blanks and side-slit configurations. These findings highlight the novelty of integrating thickness-aware forming simulations with geometric blank modification as a robust pathway for achieving near-uniform thickness and improved preform quality in thermoformed composite parts.

Abstract: The impregnation represents a crucial phase in liquid composite molding (LCM) processes. Researchers over the years have used various approaches for monitoring, based on smart weave, pressure sensors, dielectric. Among the LCM processes, the vacuum bag allows the use of visual systems for detecting the resin flow front. The integration of monitoring systems with controllers for automated management of process parameters leads to an improvement in the characteristics of the final manufactured component. In the present work, an AI-based system integrated with the control of a resin preheating system allows for improvement of the impregnation stage. A machine learning approach, based on the You Only Look Once (YOLO) algorithm, has been integrated with the visual monitoring system to detect and dynamically track the resin flow front in real time. The flow front position has been compared with the theoretical one, evaluated by using the Darcy’s law and based on the mismatch the controller suggests a proper in-time regulation of microwave power. The implemented system is capable of processing images through an AI-based algorithm and extracting the kinematic data of the flow front and integrating the information from the thermocouples and the visual system to control the microwave power.

Abstract: This work primarily focuses on the development and simulation of the Liquid Resin Infusion (LRI) process for particle-filled resins, aiming to impart additional functionalities to composite parts. The paper presents both the simulation development and the experimental tests used to establish physics-based models. The main challenge lies in understanding how particle addition affects the resin flow process. The introduction of particles increases resin viscosity, which in turn influences flow behaviour. Moreover, particle filtration by the fibrous medium changes its permeability, thereby impacting both flow dynamics and particle distribution. The materials used in the infusion process are experimentally characterised, and the resulting parameters served as inputs for the LRI process simulations. Constitutive behavior laws are implemented within the simulation tool. Simulations are then conducted using all characterized inputs and models for validation purposes. These validated models are subsequently employed to assess the infusion process performance.

Abstract: This study investigates the synergistic potential of a novel heterojunction photocatalyst for methyl orange degradation. The photocatalyst comprises iron tungstate (FeWO₄) and graphitic carbon nitride (g-C₃N₄), engineered to exploit the distinct properties of each component for enhanced photocatalytic activity. The research systematically evaluates the performance of the synthesized FeWO₄/g-C₃N₄ composite in degrading methyl orange, with an emphasis on optimizing catalytic efficiency. The photocatalyst was characterized using advanced techniques, including Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and scanning electron microscopy (SEM) to elucidate its structural and morphological properties. Key parameters such as loading concentrations were optimized to assess their influence on the photodegradation efficiency. Among tested compositions, 1.0 wt% FeWO₄/g-C₃N₄ achieved the highest degradation efficiency of MO at 78.04% within 180 minutes under UV irradiation. The heterojunction structure promoted effective charge separation, and further enhanced visible-light response. These results demonstrate the catalyst’s potential for sustainable water purification applications.

Abstract: This study presents the development of an advanced 215.9 mm drill bit with an optimized material distribution to enhance wear resistance, durability, and operational efficiency in highly abrasive formations. A comprehensive scanning electron microscopy (SEM) analysis of the powders used in the drill bit's construction was conducted using the TESCAN Mira 3 LMU system. The analysis included tungsten carbide–cobalt (WC–Co) and diamond-containing composite powders. The results revealed that WC–Co powders exhibit high density and uniform particle distribution, making them suitable for load-bearing components, while diamond-containing powders ensure superior cutting performance and wear resistance. Based on these findings, a rational material allocation was implemented: WC–Co-based materials were used for structural elements, and diamond-containing powders were applied in cutting and undercutting inserts. Process was optimized to prevent thermal degradation of the polycrystalline diamond compact (PDC) inserts. The developed drill bit was designed for rotary drilling with an axial load range of 20–80 kN, rotation speeds of 80–250 rpm, and a drilling fluid flow rate of up to 40 L/s. The proposed design is particularly suited for the geological and technical conditions of Kazakhstan’s oil and gas fields, contributing to reduced drilling costs and increased efficiency.

Abstract: Ni and Ni-containing nanoparticles exhibit promising magnetic properties. In a preliminary experiment, these nanoparticles aggregated after synthesis. Because nanoparticle aggregation may degrade their unique properties, a method to prevent their aggregation is required. In this study, Ni-Pt nanoparticles were synthesized and coated with silica to suppress aggregation. A colloidal solution of Ni-Pt nanoparticles was synthesized in water exposed to air using nickel(II) acetate tetrahydrate (Ni source), hexachloroplatinate(IV) hexahydrate (Pt source), sodium borohydride (reducing agent), and citric acid (stabilizer). Silica-coated Ni-Pt nanoparticles (Ni-Pt/SiO₂) were synthesized by adding a tetraethylorthosilicate (TEOS)/ethanol solution to the colloidal Ni-Pt nanoparticle solution. The morphology of the Ni-Pt nanoparticles varied with reaction time. The Ni-Pt/SiO₂ nanoparticles consisted of Ni-Pt cores and SiO₂ shells, with their morphology dependent on the TEOS concentration. Furthermore, the Ni-Pt/SiO₂ nanoparticles were more dispersed than the uncoated Ni-Pt nanoparticles, suggesting that the silica coating suppressed aggregation.

Abstract: In this study, we used a linear regression machine learning model to predict the stress-strain curve of AZ91/graphene composites. The proposed model successfully made predictions with an accuracy of approximately 0.99 (99%) and a small error. The mechanical properties obtained from the curves, such as the yield and ultimate tensile strength, were in excellent agreement with the actual and predicted values. This linear regression model is also well-suited for predicting the stress-strain curve of composites.

Abstract: In the production of layered steel-based composite materials by the liquid-phase method, an importance is attached to preserving the structural and physical-mechanical characteristics of the steel sheet serving as the middle layer. The temperature field in such a steel layer contacting with aluminum melt at a temperature of ~700°C in a roller-crystallizer is analyzed. A formula is obtained that can be used to determine the temperature distribution in the middle layer of steel at the initial stage of the technological process. A comparison of the theoretical results with experimental studies of the thermal modes of obtaining a layered steel–aluminum composite by the liquid-phase method is carried out.

Abstract: In₂O₃/ZnO/fluorine-doped tin oxide (FTO) photoanode was prepared by electrochemical anodization-hydrothermal approach and to assemble a visible light activated photocatalytic fuel cell (PFC) with CuO/Cu cathode. The as-fabricated electrodes were scrutinized using field-emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX) and electrochemical impedance spectroscopy (EIS) analyses. The maximum power density and chlortetracycline hydrochloride (CTCH)-bearing aquaculture wastewater removal efficiency of In₂O₃/ZnO/FTO PFC treatment for 240 min were 0.3084 µW cm^-2 and 91.5%, respectively, which were much higher than that of PFC with ZnO/FTO photoanode (0.1805 µW cm^-2 and 67.5%, respectively). The spectacular performance of this PFC system was realized by the S-scheme heterojunction of the photoanode between In₂O₃ and ZnO/FTO, which boosted the segregation of photoexcited carriers and yielded powerful active radical species for the photoelectrocatalytic activity. This study can serve as reference for the devise and heterojunction establishment of highly effective electrodes of PFC with visible light response.