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Totally found 149 items.

  • [会议] TEMPERATURE-DEPENDENT MECHANICAL RESPONSE OF CARBON NANOTUBE REINFORCED EPOXY NANOCOMPOSITES: AN ATOMISTIC SIMULATION STUDY
    A preliminary analysis of the temperature-dependent elastic and plastic response of carbon nanotube (CNT) reinforced nanocomposites using an atomistically informed approach is presented. By utilizing molecular dynamics (MD) simulations, the effects of temperature on mechanical properties have been investigated for epoxy-based polymer composites reinforced by randomly dispersed CNTs. A molecular model has been developed for the bulk matrix of the randomly dispersed CNT architecture, and virtual deformation tests have been performed to estimate mechanical properties under a wide range of temperatures. The results indicate that the strength and stiffness of these nanocomposites degrade as the temperature increases and the increase in temperature is linked to an increase in the Poisson’s ratio. This physics-based understanding of the effects of temperature and nanoconfiguration on critical mechanical properties will be valuable for the design optimization of nanocomposites.
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  • [会议] ELEMENTS AND MECHANISMS FOR APPLYING ARTIFICIAL INTELLIGENCE TO COMPOSITES FABRICATION
    The composites industry will soon be in a position to apply artificial intelligence (AI) in ways that will accelerate manufacturing and inspection processes, and also will enable rapid process and quality improvement throughout the product lifecycle. AI-enabled technology has broad-ranging applications within composites, from a specific manufacturing process to an enterprise-wide Industrial Internet of Things (IIoT) program. In a developmental AI application in 2018, a convolutional neural network (CNN) successfully generated an analysis algorithm for an automatic inspection system to detect foreign objects and debris (FOD) on critical component surfaces. This AI application will eventually replace painstaking hand-engineering of algorithms. Its success highlights not only what AI might contribute to composites fabrication, but also how AI-enabled fabrication technology might be developed. For example, the CNN trial has uncovered an immediate need for large quantities of raw data and images, which are the necessary “raw materials” of AI application development. Other needed elements and advancements will be discussed. AI development as applied to composites lends itself to incremental implementation, with benefits being realized with each increment. Both near-term and long-term benefits will be described.
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  • [会议] NONDESTRUCTIVE, NONCONTACT QUANTIFICATION OF CARBON FIBER ALIGNMENT AND ORIENTATION BY HIGH-SPEED MICROWAVE ELLIPSOMETRY
    Novel short-fiber composites facilitate the manufacture of tailorable feedstock for small formed parts. In these composites, the alignment and orientation of the short fibers must be controlled to achieve the desired composite properties. While there are several processing variables that can be correlated to fiber alignment and orientation, there is a need for a fast, nondestructive, noncontact measurement technique to quantify local alignment and orientation in real time. Such a technique would enable real-time control of processing variables, resulting in higher quality composites. Here, we propose high-speed microwave ellipsometry as such a technique. To evaluate our approach, we measured five short-fiber composites samples made from a four-layer stack of carbon-fiber mats. These samples included one known control sample and four blind samples that were unknown at the time of testing. The four blind samples were known to be either a control, a sample with all layers rotated by 5°, a sample with a single unknown layer rotated by 5°, or a sample with a single unknown layer rotated by 15°. In this paper, we present our results demonstrating the effectiveness of this technique and discuss a path for real-time, large-scale imaging of fiber alignment and orientation.
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  • [会议] USING Z-AXIS MILLED FIBERS TO TOUGHEN CARBON FIBER COMPOSITES
    Carbon fiber fabrics are surface-coated with Z-axis oriented milled carbon fibers and processed into 3-D reinforced prepregs (tradename: Carbon Supercomposite?). These milled carbon fibers provide Z-axis interlaminar reinforcement that increases compressive toughness by over 300% and compressive strength by 35% without any detriment to stiffness. These improvements are important for the construction of light yet durable bicycles that prioritize the safety of cyclists.
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  • [会议] EFFECT OF GRAPHENE ON THE FLAMMABILITY BEHAVIOR OF POLYIMIDE-GRAPHENE COMPOSITES
    The presence of graphene in the composites resulted in a remarkable decrease in the heat of combustion of the polymer by up to 81% at 50 wt.% graphene. An increase from 0- 50 wt.% graphene led to a 77% decrease in HRC indicating that the presence of graphene contributed to the increase in anti-flammability behavior of polyimide. The decrease in HRC slowed down at 30 wt.% graphene, showing only a 2.7% decrease between 30 wt.% and 50 wt.%. The rate of degradation was also shown to significantly decrease by up to 85% from 0-50 wt.% graphene.
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  • [会议] STIFFNESS PREDICTION AND VALIDATION OF LARGE VOLUME 3D PRINTED, SHORT-FIBER-FILLED POLYMER COMPOSITES
    Large-volume 3D deposition of short carbon fiber filled (CFF) thermoplastic composites greatly enhances the value and potential of additive manufacturing by offering fast production of large-scale tooling and even large-scale, end-use parts. Validated models to predict the material properties as a function of processing parameters of such 3D printed composites aid in driving down the design cost of a production part by eliminating the need to process large volumes of material in trial prints and the subsequent final product characterization. In this study, a method of predicting the effective elastic modulus from the flow simulation to the final deposition and cooling of a short fiber filled deposited structure is presented. Specifically, a 13% CFF acrylonitrile butadiene styrene (ABS) is considered. An in-house, large volume 3D printer was built and used to print tensile bars that were tested based off ASTM-D3039. Modeling was carried out using a custom MATLAB code to model the fiber orientation state along the velocity field streamlines within the nozzle, the die-swell of the extrudate, and the subsequent deposition onto the moving platen. The resulting predicted fiber orientation state is then coupled with micromechanical modeling to obtain a spatially varying anisotropic stiffness tensor. This result is then used within a finite element model with spatial varying stiffness to mimic the effective stiffness of the processed composite. Modeling results indicate little difference between a fully filled deposition (i.e., no interlaminar voids between deposition beads) and the actual cross-sectioned geometry. The results obtained from the RSC fiber interaction model for a value of =1/30 and =0.03 were in the best agreement with the experimental testing with a differential of less than 20% between experiment and modeling, and future work will be required to better characterize the flow parameters before the modeling efforts can be considered fully validated.
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  • [会议] PROCESS MODELLING OF INDUCTION WELDING FOR THERMOPLASTIC COMPOSITE MATERIALS BY NEURAL NETWORKS
    Induction welding for thermoplastic composite materials uses an alternating current flowing through a coil to induce an electromagnetic field and generate eddy current inside laminate with various fiber orientations – the generated heat causes the laminate to heat up and melt the polymer. As a pressure is applied to the induction heating zones, cohesive bonding may occur during the melting of the polymer. The welding quality of the composite materials is highly influenced by the temperature varying inside the heating zones. Thus, it is beneficial for induction welding if temperature varying during heating can be acquired given a set of welding parameters, such as current, pressure, fiber orientations, etc. Conducting practical induction heating experiments for this purpose is laborious and time consuming given the large varying space of welding parameters. In this paper, we propose to address this problem by using machine learning techniques to model the relation between the welding parameters and the temperature varying inside the heating zones. We conduct two sets of induction heating experiments for laminate welding and the collected sample temperature varying data are used to train the neural networks with input of welding parameters and output of the predicted temperature varying. Testing of the models demonstrates that process modeling of induction welding with machine learning techniques is viable.
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  • [会议] MULTISCALE APPROACHES TO FORMATION OF THERMOPLASTIC PREPREG SHORT CARBON FIBER
    The fabrication of prepreg carbon fiber typically involves continuous fibers or fabrics which cannot be easily translated to discontinuous fibers. Additionally, applying prepreg coatings typically involves resins of relatively high viscosity making facilitating traditional coating methods which are not receptive to short fibers. In this work we demonstrate the application of high performance thermoplastic polyimides to short carbon fibers. We show the development of a variety of coating techniques used to produce prepregs from the solution and melt states. The quality, thickness, and uniformity of these coatings are assessed using fluorescence and scanning electron microscopy. We illustrate the capabilities of these methods to apply coatings subject to interfacial conditions. Furthermore we show the effect of interfacial treatments (oxidation, reduction, sizing, etc.) on coating quality. Through large scale techniques, it is possible to fabricate short fiber prepregs for use in high performance composite materials.
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  • [会议] HYBRIDIZATION OF CARBON NANOTUBE-GLASS FIBER BASED HIERARCHICAL COMPOSITES USING ELECTROPHORETIC DEPOSITION
    Hybridization of nanomaterials such as carbon nanotubes with advanced textiles such as glass and carbon fiber enables the creation of hierarchical composites. Traditionally, the hierarchical composites were manufactured using chemical vapor deposition, which is an expensive and energy intensive process and may cause damage to the textiles due to extreme temperatures involved. In this research, we discuss the characterization and applications of hierarchical composites manufactured using a scalable, aqueous dispersion based electrophoretic deposition process. The carbon nanotubes (CNTS) are functionalized with a dendritic polymer polyethylenimine (PEI) which gives the nanotubes a positive charge in the aqueous dispersion. Using electric field, the positively charged PEI functionalized carbon nanotubes are deposited on the cathode. The EPD process is inherently scalable since no harsh chemicals are used and the process can be performed at room temperature. Along with being a scalable process, the key advantages of this process are its ability to coat conductive and non-conductive substrates and the ability to control the thickness of carbon nanotube coating on the surface of the fibers by varying process parameters such as time of deposition, functionalization of carbon nanotubes, electric field strength and concentration of carbon nanotubes. The mechanism of film formation using EPD is characterized and the influence of processing parameters on the film growth is investigated. A variety of fabrics such as cotton, wool, nylon, polyester and glass fiber are coated.
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  • [会议] INVESTIGATION OF INFLUENCE OF DIFFERENT PIERCING METHODS OF ABRASIVE WATERJET ON DELAMINATION OF FIBER REINFORCED COMPOSITE LAMINATE
    Due to the highly abrasive nature of composite materials, cutting composites with traditional rotary cutting tools such as a band saw or Dremel? tool is a challenge. Cutting composite materials often results in tool wear, high heat, and dust generated during the cutting process. Thermoplastic reinforced composites can be especially challenging to machine because of their tendency to melt due to the high heat generated during cutting. If the melted material gums up the cutting blade, the cutting equipment is no longer able to cut the material effectively. Abrasive waterjet cutting (AWJ) does not have direct contact between the machine and material, so many of the machining challenges are avoided. However, the impact of a waterjet stream may cause delamination of composite laminates. This research addresses the influence of different piercing methods of the waterjet cutting process on delamination of the composite laminate. The investigated geometry is a 25.4 mm x 101.6 mm carbon fiber laminate which is manufactured using 3 plies, 6 plies, and 30 plies of plain weave carbon fiber prepreg. The axial centerline of the sample will be pierced and cut using four different kinds of piercing methods; stationary piercing, dynamic piercing, low-pressure piercing, and very brittle material piercing. After cutting the sample with water jet, the delamination zone of the composite laminate will be observed using a Computed tomography (CT) scanning technology. The analysis shows that the distribution of delamination and the size of delamination depends on the different piercing methods.
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