Search

This study evaluates the influence of different loading angles and the area of loading on the ensuing stress distribution and the physical response of a natural central incisor tooth, using a 3D finite element analysis. The CAD model of the incisor tooth assembly (including enamel, dentin, periodontal ligament, pulp, gingiva and jaw bone) was subject to an external (chewing) load of 100 N, over four different areas and at four different angles along the vertical. It was observed that the tooth experiences high von-Mises equivalent stresses and high bending when the load applied is closer to the incisal edge of the crown. Also, the stresses on the dentin, in general, increased with the increase in the loading angle regardless of the area of loading; with the highest stress (~70 MPa) generated at 45° angle. The percentage change observed in dentin von-Mises stresses was higher than that of enamel when the loading angle was increased from 0° to 45°, because of the higher stiffness of enamel and structural differences in enamel and dentin. The numerical results indicated that applying loads on incisal edge would simulate a severe loading condition for the incisor tooth.

Emerging sutureless wound-closure techniques have led to paradigm shifts in wound management. State-of-the-art biomaterials offer biocompatible and biodegradable platforms enabling high cohesion (toughness) and adhesion for rapid bleeding control as well as robust attachment of implantable devices. Tough bioadhesion stems from the synergistic contributions of cohesive and adhesive interactions. This Review provides a biomacromolecular design roadmap for the development of tough adhesive surgical sealants. We discuss a library of materials and methods to introduce toughness and adhesion to biomaterials. Intrinsically tough and elastic polymers are leveraged primarily by introducing strong but dynamic inter- and intramolecular interactions either through polymer chain design or using crosslink regulating additives. In addition, many efforts have been made to promote underwater adhesion

Electronically optimized bis-diazirine crosslinkers allow aliphatic polymers to be crosslinked with up to 10-fold improved efficacy, relative to earlier designs. Activation is achieved using modest temperatures or through UV or visible light.

Thermophysiological comfort is known to play a primary role in maintaining thermal balance, which corresponds to a person’s satisfaction with their immediate thermal environment. Among the existing test methods, sweating torsos are one of the best tools to provide a combined measurement of heat and moisture transfer using non-isothermal conditions. This study presents a preliminary numerical model of a single sector sweating torso to predict the thermophysiological comfort properties of fabric systems. The model has been developed using COMSOL Multiphysics, based on the ISO 18640-1 standard test method and a single layer fabric system used in sportswear. A good agreement was observed between the experimental and numeral results over different exposure phases simulated by the torso test (R2 = 0.72 to 0.99). The model enables a systematic investigation of the effect of fabric properties (thickness, porosity, thermal resistance, and evaporative resistance), environmental conditions (relative humidity, air and radiant temperature, and wind speed), and physiological parameters (sweating rate) to gain an enhanced understanding of the thermophysiological comfort properties of the fabric system.

The emerging advancements in separation and classification of various biological matters (e.g., living cells and proteins) using magnetic levitation (MagLev) technology have proven to be effective for improving disease diagnostics. MagLev technique has the capacity to detect and separate useful diagnostic biomarkers from biocomplex environments (e.g., blood and plasma), minimizing the unpleasant daunting task of sample preparations and labeling procedures. Here, we demonstrate the capability of this technique combined with image analysis and machine learning approaches for discriminating the various types of multiple sclerosis (MS) as an important model disease. To arrive at a systematic expert system, we combined robust statistical analysis with machine learning to (1) detect and remove outliers from the raw MagLev image datasets; then (2) process the images and output a low dimensional representation of massive data without losing the main statistical features; and finally (3) predict the MS clinical disease type (Relapsing-Remitting, Primary–Progressive, or Secondary–Progressive) using a classifier. This is expected to improve MS diagnostics since the current practices rely solely on clinical observation and central nervous system imaging, making management approaches are often reactional and inefficient. Thus, there is a need to identify the disease type early on. MagLev is expected to improve MS diagnostics, thereby aiding in prognosis and guiding adequate treatment choices before the patient exhibits signs of permanent neurological deficits.

In this study, the process of thermoplastic material deposition during 3D printing has been investigated via a multiphysics approach, using numerical and experimental tools. Namely, through computational fluid dynamics modeling, details of the flow structure and heat transfer process of the deposited material from the nozzle head onto the bed have been investigated. Experimental trials were conducted to validate the results of the computational model, and a good conformity between the results was obtained, with error less than 8%. Both curling and fluid mechanical swing machine print patterns were captured, especially given the high-viscosity and non-Newtonian characteristics of the flow. In particular, in the conducted case on ABS, results indicated that the ratio of the extrudate diameter to the nozzle diameter is around 12%. At feed rates lower than 100 mm/min, the curling takes place, where the extruded material bends up against the direction of the injection at the nozzle. It was observed that the fluid mechanical swing machine pattern takes place at the feed rate-to-moving bed speed ratios higher than 1.0, and depending on the nozzle to bed gap. High fidelity numerical platforms are deemed critical to better comprehend and predict complex local phenomena taking place during fused filament fabrication, and accordingly to more reliably optimize the process parameters.

The aim of the present work is to study the suppressed torsional vibration of shafts, with a clamped boundary condition at either end, via single or multiple undamped absorbers. Structures with such features often suffer from failures (e.g. fatigue, wear) caused by excessive vibrations. The shaft considered is under the action of a harmonic point moment. A vibration design benchmark for torsional absorbers is presented. For both cases of the shaft with one and two absorbers, the variation of absorber parameters, including attaching position, moment of inertia and torsional stiffness, are investigated. The optimal values of the absorber control parameters are obtained. First, the governing equation for the torsional vibration of the shaft and its absorbers is derived in a dimensionless form. Next, the free and forced vibrations of the system are investigated and dimensionless natural frequencies are obtained. To absorb the identified natural frequencies, we compute the $${L}_{2}$$ norm of the angular displacement of the shaft at these frequencies and tune the parameters of the absorbers to minimize the norm. A detailed analytical solution framework for determining the torsional vibration response of a shaft equipped with torsional vibration absorbers is developed. Minimizing the established relations for the $${L}_{2}$$ norm of the shaft’s torsion is examined for a wide range of frequencies, as a criterion for determining its absorption frequency. Advantage of using two absorbers over a single absorber is highlighted. Validation of the analytical model is achieved by comparisons to both numerical and experimental results.

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.

Temperature and humidity measurements in electrochemical energy devices are essential for maximizing their overall performance under different operating conditions and avoiding hazardous consequences that may arise from the malfunction of these systems. Using sensors for in situ measurements of temperature and relative humidity (RH) is a promising approach for continuous monitoring and management of electrochemical power sources. Here, we report on the feasibility of using thread-based sensors for in situ measurements of temperature and RH in proton exchange membrane fuel cells (PEMFCs) as an example of electrochemical energy devices. Commodity threads are low-cost and flexible materials that hold great promise for the creation of complex three-dimensional (3D) circuits using well-established textile methods such as weaving, braiding, and embroidering. Ex situ and in situ characterization show that threads can be introduced in the gas diffusion layer (GDL) structure to inscribe water highways within the GDL with minimal impact on the GDL microstructure and transport properties. Fluorinated ethylene propylene (FEP) is coated on thread-based sensors to decouple the response to temperature and humidity; the resulting threads achieve a linear change of resistance with temperature (−0.31%/°C), while RH is monitored with a second thread coated with poly(dimethylsiloxane) (PDMS). The combination of both threads allows for minimally invasive and dynamically responsive monitoring of local temperature and RH within the electrode of PEMFCs.

Composite materials have been widely used in the aerospace industry and are critical for safe operations. However, the delamination, caused by cyclic loads and corrosive service environment, poses a serious threat to the structural integrity of composite laminates. The acoustic emission technique has been adopted to assess the structural integrity by characterizing damage location, type, and size. This article proposes and compares data-driven prognostic methods to quantify the delamination area efficiently and accurately. To address the problem of insufficient inspection data, the prediction model adopts the path length across the delamination area as the target value. The delamination area can then be estimated with the predicted path length based on formulated geometric relationships. This solution will augment the model training datasets, and consequently, avoid the overfitting problem during the training process. Experimental results on composite coupons demonstrate that the proposed ensemble learning-based model outperforms other state-of-the-art methods in terms of prediction accuracy and efficiency.

Inter-ply slippage is known to be an important mechanism taking place during forming processes of textile composites, especially with respect to multi-layer fabric lay-ups. The coefficient of friction between the plies strongly depends on the structure and the orientation of forming fabric. Despite many numerical and experimental investigations, this dependency and its effect on the interaction between the plies has been overlooked. In this paper, the effect of fiber orientation on the interlayer friction of a typical thermoplastic fabric prepreg at room temperature is investigated. Results, through a polar representation of data, revealed that both static and dynamic coefficients of friction are statistically dependent on the lay-up orientation, applied normal load, along with their interaction. Further, it was identified that the repeated frictional loading of the plies results in a hysteresis, particularly for asymmetric layups due to non-negligible movement and realignment of filaments at the micro-scale. Finally, an empirical model was developed using an interpolation function (for pressure dependency) combined with a Fourier series (for orientation dependency), to predict the coefficients of friction.

The manufacturing of composite structures is a highly complex task with inevitable risks, particularly associated with aleatoric and epistemic uncertainty of both the materials and processes, as well as the need for in-situ decision-making to mitigate defects during manufacturing. In the context of aerospace composites production in particular, there is a heightened impetus to address and reduce this risk. Current qualification and substantiation frameworks within the aerospace industry define tractable methods for risk reduction. In parallel, Industry 4.0 is an emerging set of technologies and tools that can enable better decision-making towards risk reduction, supported by data-driven models. It offers new paradigms for manufacturers, by virtue of enabling in-situ decisions for optimizing the process as a dynamic system. However, the static nature of current (pre-Industry 4.0) best-practice frameworks may be viewed as at odds with this emerging novel approach. In addition, many of the predictive tools leveraged in an Industry 4.0 system are black-box in nature, which presents other concerns of tractability, interpretability and ultimately risk. This article presents a perspective on the current state-of-the-art in the aerospace composites industry focusing on risk reduction in the autoclave processing, as an example system, while reviewing current trends and needs towards a Composites 4.0 future.