The Effect of Material and Processing on the Mechanical Response of Vapor-Grown Carbon Nanofiber/Vinyl Ester Composites

The effects of material/fabrication parameters on vapor-grown carbon nanofiber (VGCNF) reinforced vinyl ester (VE) nanocomposite flexural moduli and strengths were investigated. Statistically reliable empirical response surface models were developed to quantify the effects of VGCNF type, use of dispersing agent, mixing method, and VGCNF loading on flexural properties. Optimal nanocomposite formulation and processing (0.74 phr oxidized VGCNFs, dispersing agent, and high-shear mixing) resulted in predicted flexural modulus and strength values 1.18 and 1.26 times those of the neat resin. Additional flexural, tensile, and compressive tests were performed for optimally configured nanocomposites cured in a nitrogen environment. While flexural and tensile moduli significantly increased with increasing VGCNF loading, the corresponding strengths fell below those of the neat resin. In contrast, nanocomposite ultimate compressive strengths significantly exceeded the neat resin strengths. Nanocomposites prepared using aggressive high-shear mixing displayed improved elastic moduli and substantially increased strengths relative to nanocomposites prepared using baseline methods

Experimental Studies and Finite Element Modeling Of Lightning Damage to Carbon/Epoxy Laminated and Stitched Composites

Lightning damage resistance of unstitched carbon/epoxy laminates and a Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) panel were characterized by laboratory-scale lightning strike tests and multiphysics-based lightning strike finite element (FE) models. This dissertation combines three related research topics: (1) a three-dimensional (3D) heat transfer problem, (2) lightning damage resistance assessments of carbon/epoxy laminates, and (3) lightning damage resistance of PRSEUS panel. The first project deals with a 3D analytical heat transfer problem as a solid foundation for understanding the steady-state temperature distribution in an anisotropic composite heat spreader. The second project characterizes lightning damage to unprotected carbon/epoxy laminates and laminates with either copper mesh (CM) or pitch carbon fiber paper (PCFP) protection layers subjected to standard impulse current waveforms, consistent with actual lightning waveforms, with 50, 125, and 200 kA nominal peak currents. Multiphysics-based FE models were developed to predict matrix thermal decomposition (a primary form of lightning damage) in unprotected, CM-protected, and PCFP-protected carbon/epoxy laminates. The predicted matrix decomposition domains in the damaged laminates showed good agreement with experimental results available in the literature. Both the CM and the PCFP lightning protection layers successfully mitigated lightning damage development in the underlying composites. The third project includes lightning damage characterization of a PRSEUS panel. Laboratory-scale lightning strike tests with nominal 50, 125, and 200 kA peak currents were performed at the mid-bay, stringer, frame, and frame/stringer intersection locations of the PRSEUS panel. The elliptical regions of intense local damage were elongated along the outermost lamina’s carbon fiber direction, consistent with observations from the unstitched carbon/epoxy laminates. However, the damaged PRSEUS panel exhibited unique damage features due to use of warp-knitted fabrics and through-thickness VectranTM stitches. The polyester threads used to weave the warp-knitted laminates locally confined small-scale fiber damage. This resulted in somewhat periodic and scattered small tufts of carbon fibers near the lightning attachments. Through-thickness VectranTM stitches also confined intense local damage development at the stringer and frame locations. The polyester warp-knit fabric skins and through-thickness VectranTMstitches have a significant beneficial effect on lightning damage development on a PRSEUS panel

Predicting Stochastic Lightning Mechanical Damage Effects on Carbon Fiber Reinforced Polymer Matrix Composites

Three stochastic air blast models are developed with spatially varying elastic properties and failure strengths for predicting lightning mechanical damage to AS4/3506 carbon/epoxy composites subjected to \u3c 100 kA peak currents: (1) the conventional weapon effects program (CWP) model, (2) the coupled eulerianlagrangian (CEL) model, and (3) the smoothed-particle hydrodynamics (SPH) model. This work is an extension of our previous studies [1–4] that used deterministic air blast models for lightning mechanical damage prediction. Stochastic variations in composite material properties were generated using the Box-Muller transformation algorithm with the mean (i.e., room temperature experimental data) and their standard deviations (i.e., 10% of the mean herein as reference). The predicted dynamic responses and corresponding damage initiation prediction for composites under equivalent air blast loading were comparable for the deterministic and stochastic models. Overall, the domains with displacement, von-Mises stress, and damage initiation contours predicted in the stochastic models were somewhat sporadic and asymmetric along the fiber’s local orientation and varied intermittently. This suggests the significance of local property variations in lightning mechanical damage prediction. Thus, stochastic air blast models may provide a more accurate lightning mechanical damage approximation than traditional (deterministic) air blast models. All stochastic models proposed in this work demonstrated satisfactory accuracy compared to the baseline models, but required substantial computational time due to the random material model generation/assignment process, which needs to be optimized in future work

Design of Composite Double-Slab Radar Absorbing Structures Using Forward, Inverse, and Tandem Neural Networks

The survivability and mission of a military aircraft is often designed with minimum radar cross section (RCS) to ensure its long-term operation and maintainability. To reduce aircraft’s RCS, a specially formulated Radar Absorbing Structures (RAS) is primarily applied to its external skins. A Ni-coated glass/epoxy composite is a recent RAS material system designed for decreasing the RCS for the X-band (8.2 – 12.4 GHz), while maintaining efficient and reliable structural performance to function as the skin of an aircraft. Experimentally measured and computationally predicted radar responses (i.e., return loss responses in specific frequency ranges) of multi-layered RASs are expensive and labor-intensive. Solving their inverse problems for optimal RAS design is also challenging due to their complex configuration and physical phenomena. An artificial neural network (ANN) is a machine learning method that uses existing data from experimental results and validated models (i.e., transfer learning) to predict complex behavior. Training an ANN can be computationally expensive; however, training is a one-time cost. In this work, three different Three ANN models are presented for designing dual slab Ni-coated glass/epoxy composite RASs: (1) the feedforward neural network (FNN) model, (2) the inverse neural network (INN) model – an inverse network, which maintains a parallel structure to the FNN model, and (3) the tandem neural network (TNN) model – an alternative to the INN model which uses a pre-trained FNN in the training process. The FNN model takes the thicknesses of dual slab RASs to predict their returns loss in the X-band range. The INN model solves the inverse problem for the FNN model. The TNN model is established with a pre-trained FNN to train an INN that exactly reverses the operation done in the FNN rather than solving the inverse problem independently. These ANN models will assist in reducing the time and cost for designing dual slab (and further extension to multi-layered) RASs

Identifying Fibre Orientations for Fracture Process Zone Characterization in Scaled Centre-Notched Quasi-Isotropic Carbon/Epoxy Laminates with a Convolutional Neural Network

This paper presents a novel X-ray Computed Tomography (CT) image analysis method to characterize the Fracture Process Zone (FPZ) in scaled centre-notched quasi-isotropic carbon/epoxy laminates. A total of 61 CT images of a small specimen were used to fine-tune a pre-trained Convolutional Neural Network (CNN) (i.e., VGG16) to classify fibre orientations. The proposed CNN model achieves a 100% accuracy when tested on the CT images of the same scale as the training set. However, the accuracy drops to a maximum of 84% when tested on unlabelled images of the specimens having larger scales potentially due to their lower resolutions. Another code was developed to automatically measure the size of the FPZ based on the CNN identified 0°plies in the largest specimen which agrees well with the manual measurement (on average within 3.3%). The whole classification and measurement process can be automated without human intervention

PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM

Text-to-image generation model is able to generate images across a diverse range of subjects and styles based on a single prompt. Recent works have proposed a variety of interaction methods that help users understand the capabilities of models and utilize them. However, how to support users to efficiently explore the model's capability and to create effective prompts are still open-ended research questions. In this paper, we present PromptCrafter, a novel mixed-initiative system that allows step-by-step crafting of text-to-image prompt. Through the iterative process, users can efficiently explore the model's capability, and clarify their intent. PromptCrafter also supports users to refine prompts by answering various responses to clarifying questions generated by a Large Language Model. Lastly, users can revert to a desired step by reviewing the work history. In this workshop paper, we discuss the design process of PromptCrafter and our plans for follow-up studies.Comment: 5 pages, AI & HCI Workshop at the 40 International Conference on Machine Learning (ICML) 202

Advanced structural health monitoring in carbon fiber-reinforced plastic using real-time self-sensing data and convolutional neural network architectures

In this study, advanced structural health monitoring (SHM) using a non-destructive self-sensing method-ology was proposed for large-sized carbon fiber-reinforced plastic (CFRP). Cyclic point bending tests were performed on three types of CFRPs. The damage severity identification and localization were classified and investigated using four different convolutional neural network (CNN) architectures. Electrical resis-tance images were used to train each CNN architecture for damage analysis. An optimized CNN architec-ture for the damage analysis of CFRPs using electrical resistance data was proposed and compared with traditional damage analysis CNN architectures. The applicability of the proposed SHM methodology was verified by analyzing unseen damage in the CFRPs. This study addresses the limitations of previous self-sensing methods by reducing the number of electrodes, which reduces data complexity and increases the sensible area of CFRPs. Thus, this study successfully designed an efficient SHM methodology with a high accuracy of over 90 % for analyzing CFRP damage, including the severity and location, regardless of the type of carbon fiber and stacking sequence of composite structures that showed high applicability.(c) 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)

Experimental and Numerical Study of Low-Velocity Impact Damage in Sandwich Panel with UHMWPE Composite Facings

This paper is concerned with the low-velocity impact (LVI) response behaviour of sandwich composite panels (SCPs) with ultra-high molecular weight polyethylene (UHMWPE) composite facings and Polyvinyl Chloride (PVC)/Polyethylene Terephthalate (PET) foam cores. A series of LVI tests with SCPs subjected to 50 J, 80 J and 110 J were conducted to examine their impact characteristics and damage mechanisms. LVI-induced internal damage in the SCPs were characterised by compute micro-tomography (μCT) analysis. The effects of UHMWPE areal density and foam type on the LVI responses and associated failure modes of the panels were also examined. The experimental results showed that the SCP with a PET foam core exhibited higher impact strength and energy absorption performance than those of the panel with a PVC foam core. In addition, a finite element (FE) model incorporating the Puck’s failure criteria, cohesive law and crushable foam plasticity model was developed and validated to predict the intra- and inter-laminar damages of SCPs. Finally, several failure mechanisms (fibre failure, matrix cracking and local delamination) of SCPs during LVI was thoroughly discussed. The results show the UH170-PET specimen has the best impact resistance and energy absorption performance. The parametric analysis of the areal density and foam type has revealed that these parameters can be optimised for the best LVI resistance of SCPs. These findings are helpful for designing lightweight foam-based sandwich composite structures with superior impact resistance