Showing papers by "Fuh-Gwo Yuan published in 2019"

3D printing of electroactive PVDF thin films with high β-phase content

Abstract: Bi-axially oriented electroactive PVDF thin films were prepared by a 3D printing process during which multiple parameters play a critical role in enhancing β-phase and crystallinity of the thin films. The PVDF molecular chains were rotated and stretched to form orderly arrangement by the pulling of in situ strong auxiliary electric potential (voltage) and the mechanical pulling force, and the aligned PVDF molecular chains improved the crystallinity of the thin films. Doping very small amount of multi-walled carbon nanotubes (MWCNTs) or graphene (GR) as a nucleating agent significantly increased the content of the β-phase in the films. Adding GR behaved is better than the MWCNTs in improving the β-phase due to the lamellar structure of graphene can generate a large number of micro electric fields stimulating the transformation of molecular chain from trans-gauche-trans-gauche(TG+TG−) to all-trans (TTTT). The content of β-phase in the PVDF/GR(0.03 wt%) composite thin film researched at 61.52%. Under the special environment of 3D printing, the printed PVDF thin films became very dense with high β-phase after depositing layer-by-layer. It exhibited good piezoelectric properties without post-poling treatment and mechanical stretching.

Guided Wave-based System for Real-time Cure Monitoring of Composites using Piezoelectric Discs and Phase-shifted Fiber Bragg Gratings.

Abstract: A real-time, in-process cure monitoring system employing a guided wave-based concept for carbon fiber reinforced polymer (CFRP) composites was developed. The system included a single piezoelectric disc that was bonded to the surface of the composite for excitation, and an embedded phase-shifted fiber Bragg grating (PS-FBG) for sensing. The PS-FBG almost simultaneously measured both quasi-static strain and the ultrasonic guided wave-based signals throughout the cure cycle. A traditional FBG was also used as a base for evaluating the high sensitivity of the PS-FBG sensor. Composite physical properties (degree of cure and glass transition temperature) were correlated to the amplitude and time of arrival of the guided wave-based measurements during the cure cycle. In addition, key state transitions (gelation and vitrification) were identified from the experimental data. The physical properties and state transitions were validated using cure process modeling software (e.g., RAVEN®). This system demonstrated the capability of using an embedded PS-FBG to sense a wide bandwidth of signals during cure. The distinct advantages of a fiber optic-based system include multiplexing of multiple gratings along a single optical fiber, small size compared to piezoelectric sensors, ability to embed or surface mount, utilization in harsh environments, electrically passive operation, and electromagnetic interference (EMI) immunity. The embedded PS-FBG fiber optic sensor can monitor the entire life-cycle of the composite structure from curing, post-cure/assembly, and in-service creating "smart structures".

Hygro-thermal wave propagation in functionally graded double-layered nanotubes systems

Abstract: In this paper, wave propagation is studied and analyzed in double-layered nanotubes systems via the nonlocal strain gradient theory. To the author\'s knowledge, the present paper is the first to investigate the wave propagation characteristics of double-layered porous nanotubes systems. It is generally considered that the material properties of nanotubes are related to the porosity and hygro-thermal effects. The governing equations of the double-layered nanotubes systems are derived by using the Hamilton principle. The dispersion relations and displacement fields of wave propagation in the double nanotubes systems which experience three different types of motion are obtained and discussed. The results show that the phase velocities of the double nanotubes systems depend on porosity, humidity change, temperature change, material composition, non-local parameter, strain gradient parameter, interlayer spring, and wave number.

An Efficient Augmented Reality (AR) System for Enhanced Visual Inspection

Abstract: While manual visual inspection of structures has the advantage of being relatively simple and low cost, it is usually time consuming, labor intensive and highly subjective. Augmented reality (AR), because of its ability to provide the user with additional information about the working environment in real-time, has been used in the past to address some of the limitations of manual visual inspection by supporting human workers during the inspection process. The paper presents the development of an efficient deep learning (DL) based augmented reality (AR) system for identifying critical departures from the pristine state of the structure with focus on two anomaly categories- corrosion and fatigue cracks. Most of the common AR devices usually come with a built-in camera for capturing image/video data, a storage and a microprocessor. However, due to the limited processing power, the underlying deep learning (DL) model has to be first trained externally and a suitable version of the trained model is then deployed locally on the device. The model then outputs information for identifying critical departures from the pristine state of the structure e.g., highlighting corroded regions, fatigue cracks and/or combination of both. This information is overlaid real time over the current field of view through either a headmounted or a hand-held AR device in order to augment the human vision. The worker can then focus on the highlighted region for a more detailed inspection. The feasibility of the proposed AR system is demonstrated using laboratory inspection of common mechanical components likes pipes, plates etc. In order to enable the model to keep learning based on the inputs from the AR glasses, a strategy for federated learning is introduced towards the end of the paper.

A Deep Learning Approach for Impact Diagnosis

Abstract: Impact on a structure generates an elastic wave that propagates through the structure carrying a wealth of information about the impact event. The paper presents a deep learning (DL) approach for analyzing these wavefields for the purpose of impact diagnosis i.e., identifying the impact location and reconstructing the impact force time-history. Unlike traditional object detection in computer vision, the nature of the impact diagnosis problem requires capturing context from the wavefield evolution i.e., it involves learning across multiple time frames of the wavefield rather than focusing only on a single stationary frame at a given moment. While scanning across multiple time frames provides essential information about the wave propagation phenomenon in terms of its interactions with structural features, boundaries etc., it mandates the use of deep learning models that can analyze this complex wave propagation phenomenon in both spatial and temporal regimes. A unified CNN-RNN network architecture proposed in the paper first reveals the impact location and then utilizes that information to reconstruct the impact force timehistory. The proposed approach is verified using simulated wavefields obtained from the finite element analysis of a five-bay stiffened aluminum panel. Even with moderate network complexity, the proposed model predicts the impact location and impact force time-history with reasonable accuracy. The potential extension of the proposed methodology to an end-to-end vision-based impact monitoring system is also discussed towards the end of the paper.

Demonstration of Prognostics Health Monitoring (PHM) in Adhesive Lap Joints using Simulated Studies

Abstract: Adhesively bonded joints are increasingly used in structural applications due to many advantages over classical mechanical fasteners. However, they are susceptible to fatigue damage due to the hostile working environment. It is essential to detect, quantify the damage and estimate the remaining useful life (RUL) under fatigue loading. This paper presents prognostics health monitoring (PHM) framework that can quantify the damage and estimate the RUL in adhesive lap joints. A PHM framework is the synthesis of four disciplines: damage diagnostics, predictive modeling, uncertainty quantification, and uncertainty propagation. Damage diagnostics provide the damage evolution in terms of damage growth rate as input to PHM system. In this work, a new diagnostic method based on ultrasonic Lamb waves is proposed for in-situ measurements of crack length in a single lap joint (SLJ). The idea is to excite single mode using two piezo transducers and extract the wave packet reflected from the crack tip to estimate crack length. The proposed method is verified using computational simulations. A predictive model must be capable of simulating the damage growth physics and can govern the growth rate using model parameters. In this study, the cohesive zone model (CZM) is used to simulate crack growth in SLJ. Uncertainty quantification methods require the evaluation of the predictive model for large parameter sets. To achieve this, setup is built using Python script to run crack propagation simulations in ABAQUS. Convergence issues and computational challenges associated with the setup is addressed. Finally, the procedure to estimate RUL using diagnostics data, predictive model and uncertainty quantification methods is discussed.

Piezoelectric characterization of boron nitride nanotube-polyurethane composites

Abstract: Boron nitride nanotubes (BNNTs) have exceptional thermal stability, thermal conductivity, mechanical properties, neutron radiation shielding, and piezoelectricity. Due to their multifunctional properties, BNNTs are potential candidates for sensory materials in harsh environments. Brittleness and non-conformity of conventional piezoelectric ceramics have limited their broad applications. Flexible and ultra-light piezoelectric sensors based on BNNTs could be an alternative solution in high temperature, high radiation, high shock, and severe vibration environments. In this study, BNNTPolyurethane (PU) composites were fabricated and their converse piezoelectric constant of d33 was assessed using a laser Doppler vibrometer (LDV). This study demonstrated that BNNT could be an excellent piezoelectric nanofiller for flexible sensor applications.

Damage detection and localization using random decrement technique on metallic plates

Abstract: Technique with the capability of detecting and localizing damage of structures using naturally operating environments can provide a possibility of developing more efficient and simpler structural health monitoring systems. This passive sensing technique would eliminate the need of active actuation which requires power either from battery or ambients to generate controlled excitation source. In a recent study, self-Green’s functions (GF) were reconstructed using auto-correlation (AC), combined with a damage index by comparing the differences in GFs between damaged and pristine metallic panels to locate the damage. In this paper, random decrement (RD) technique is proposed to reconstruct GF with computational efficiency. While the RD has been widely used for damage detection and structure parameter extraction in civil structures, in the frequency usually below 1 kHz; this study explores using RD up to 15 kHz for transient wave reconstruction and then damage localization. The concept is first validated through simulation for a plate structure, and the results show that the reconstructed self-Green’s function match well with the one from the auto-correlation technique after approximately 10,000 averages of the RD signatures.