Search

This paper aims to compare two ceramic materials available for additive manufacturing (AM) processes-vat photopolymerization (VPP) and material extrusion (MEX)-that result in fully ceramic parts after proper heat treatment. The analysis points out the most significant differences between the structural and mechanical properties and the potential application of each AM technology. The research revealed different behaviors for the specimens obtained via the two mentioned technologies. In the case of MEX, the specimens exhibited similar microstructures before and after heat treatment. The sintering process did not affect the shape of the grains, only their size. For the VPP specimens, directly after the manufacturing process, irregular grain shapes were registered, but after the sintering process, the grains fused, forming a solid structure that made it impossible to outline individual grains and measure their size. The highest compression strength was 168 MPa for the MEX specimens and 81 MPa for the VPP specimens. While the VPP specimens had half the compression strength, the results for the VPP specimens were significantly more repeatable.

Batteries are essential in modern society as they can power a wide range of devices, from small household appliances to large-scale energy storage systems. Safety concerns with traditional lithium-ion batteries prompted the emergence of new battery technologies, among them solid-state batteries (SSBs), offering enhanced safety, energy density, and lifespan. This paper reviews current state-of-the-art SSB electrolyte and electrode materials, as well as global SSB market trends and key industry players. Solid-state electrolytes used in SSBs include inorganic solid electrolytes, organic solid polymer electrolytes, and solid composite electrolytes. Inorganic options like lithium aluminum titanium phosphate excel in ionic conductivity and thermal stability but exhibit mechanical fragility. Organic alternatives such as polyethylene oxide and polyvinylidene fluoride offer flexibility but possess lower ionic conductivity. Solid composite electrolytes combine the advantages of inorganic and organic materials, enhancing mechanical strength and ionic conductivity. While significant advances have been made for composite electrolytes, challenges remain for synthesis intricacies and material stability. Nuanced selection of these electrolytes is crucial for advancing resilient and high-performance SSBs. Furthermore, while global SSB production capacity is currently below 2 GWh, it is projected to grow with a >118% compound annual growth rate by 2035, when the potential SSB market size will likely exceed 42 billion euros.

This paper proposes a novel approach to predicting the useful life of rotating machinery and making fault diagnoses using an optimal blind deconvolution and hybrid invertible neural network. First, a new optimal adaptive maximum second-order cyclostationarity blind deconvolution (OACYCBD) is developed for denoising vibration signals obtained from rotating machinery. This technique is obtained from the optimization of traditional adaptive maximum second-order cyclostationarity blind deconvolution (ACYCBD). To optimize the weights of conventional ACYCBD, the proposed method utilizes a probability density function (PDF) of Monte Carlo to assess fault-related incipient changes in the vibration signal. Cross-entropy is used as a convergence criterion for denoising. Because the denoised signal carries information related to the health of the rotating machinery, a novel health index is calculated in the second step using the peak value and square of the arithmetic mean of the signal. The novel health index can change according to the degradation of the health state of the rotating bearing. To predict the remaining useful life of the bearing in the final step, the health index is used as input for a newly developed hybrid invertible neural network (HINN), which combines an invertible neural network and long short-term memory (LSTM) to forecast trends in bearing degradation. The proposed approach outperforms SVM, CNN, and LSTM methods in predicting the remaining useful life of bearings, showcasing RMSE values of 0.799, 0.593, 0.53, and 0.485, respectively, when applied to a real-world industrial bearing dataset.

To prevent potential instability the early detection of cracks is imperative due to the prevalent use of concrete in critical infrastructure. Automated techniques leveraging artificial intelligence, machine learning, and deep learning as the traditional manual inspection methods are time-consuming. The existing automated concrete crack detection algorithms, despite recent advancements, face challenges in robustness, particularly in precise crack detection amidst complex backgrounds and visual distractions, while also maintaining low inference times. Therefore, this paper introduces a novel ensemble mechanism based on multiple quantized You Only Look Once version 8 (YOLOv8) models for the detection and segmentation of cracks in concrete structures. The proposed model is tested on different concrete crack datasets yielding enhanced segmentation results with at least 89.62% precision and intersection over a union score of 0.88. Moreover, the inference time per image is reduced to 27 milliseconds which is at least a 5% improvement over other models in the comparison. This is achieved by amalgamating the predictions of the trained models to calculate the final segmentation mask. The noteworthy contributions of this work encompass the creation of a model with low inference time, an ensemble mechanism for robust crack segmentation, and the enhancement of the learning capabilities of crack detection models. The fast inference time of the model renders it appropriate for real-time applications, effectively tackling challenges in infrastructure maintenance and safety.

Achieving glycemic control and sustaining functional pancreatic β-cell activity remains an unmet medical need in the treatment of type 2 diabetes mellitus (T2DM). Glucokinase activators (GKAs) constitute a class of anti-diabetic drugs designed to regulate blood sugar levels and enhance β-cell function in patients with diabetes. A significant progression in GKA development is underway to address the limitations of earlier generations. Dorzagliatin, a dual-acting GKA, targets both the liver and pancreas and has successfully completed two phase III trials, demonstrating favorable results in diabetes treatment. The hepato-selective GKA, TTP399, emerges as a strong contender, displaying clinically noteworthy outcomes with minimal adverse effects. This paper seeks to review the current literature, delve into the mechanisms of action of these new-generation GKAs, and assess their efficacy and safety in treating T2DM based on published preclinical studies and recent clinical trials.

The planetary gearbox plays a vital role in a wide range of mechanical power transmission systems, including high-speed trains, wind turbines, vehicles, and aircraft. At the same time, the planetary gear train inside the gearbox is regarded as the most susceptible to failure in the entire transmission system. To analyze the influence of surface roughness on the dynamic characteristics of the planetary gear train, a dynamic modeling method based on fractal theory is proposed. Firstly, the tooth surface contact model was established based on the W-M fractal function, and the time-varying mesh stiffness (TVMS) of the planetary gear train was calculated under healthy and tooth macro-pitting. Then, the lumped-parameter method is introduced to construct a planetary gear train translation-torsion dynamic model that comprehensively considers TVMS and tooth backlash. The vibration acceleration signals of the planetary gear train under different macro-pitting states and surface roughness are simulated and calculated, allowing a quantificative analysis of the influence of surface roughness on system vibration response. Finally, the correctness of the model for the planetary gear train is verified by experiments. The results show that compared with the planetary gear train modeling method based on Hertz contact theory, the root mean squared error of the vibration signal of this work under a macro-pitting fault state is reduced by 8.7%, further improving the reliability of the model. [ABSTRACT FROM AUTHOR]

This study designs an advanced single-loop output feedback system for speed servo drive applications, in which a simple proportional–integral–integral (PII) controller equipped with nonlinear feedback and feed-forward gains is formed. The resultant feedback system shows the desired critically damped performance for wide-operating regions by actively handling the system parameter and load uncertainties. There are three contributions: first, the third-order observer estimates, independent from the system model, where the speed and acceleration are obtained using the position measurement with the order reduction property; second, the observer-based PII controller is compensated by active damping with a nonlinearly structured feedback and feed-forward gains; and, third, a guarantee is achieved on the desired critically damped performance through a closed-loop analysis. A hardware testbed that adopts a 500 W brushless DC motor is used to experimentally demonstrate performance improvements over certain constant torque regions under various scenarios. [ABSTRACT FROM AUTHOR]

This paper investigates an improved fixed-time stability theory together with a state feedback controller for a class of nonlinear stochastic systems. First, a delicate transformation is performed, and next, a Gamma function is utilized to directly derive the value of the integral function, which ultimately yields a fixed-time stabilization theorem with a higher precision upper bound for the settling time. Unlike the existing estimation process of amplifying twice, we only performed one amplification, which weakens the effect of amplification. Then, a state feedback controller is constructed for stochastic systems by the method of adding a power integrator. Utilizing the proposed stochastic fixed-time stability theory, simulations show that the intended controller ensures that the trivial solution of the suggested system is fixed-time stable in probability. The results of the simulation demonstrate that the suggested control scheme is meaningful. [ABSTRACT FROM AUTHOR]

Nonskeletal animals such as worms achieve locomotion via crawling. We consider them as an inspiration to design robots that help underline the mechanisms of crawling. In this paper, we aim to identify an approach with the simplest structure and actuators. Our robots consist of cut-and-fold bodies equipped with pneumatically-driven soft actuators. We have developed fabrication techniques for coin-sized robots. Experiments showed that our robots can move up to 4.5 mm/s with straight motion (i.e., 0.1 body lengths per second) and perform cornering and U-turns. We have also studied the friction characteristics of our robots with the ground to develop a multistate model with stick–slip contact conversions. Our theoretical analyses depict comparable results to experiments demonstrating that simple and straightforward techniques can illustrate the crawling mechanism. Considering the minimal robots' structure, this result is a critical step towards developing miniature crawling robots successfully. [ABSTRACT FROM AUTHOR]

The non-variable stiffness of the flexible hinge in the fast-steering mirror (FSM) cannot adapt to varying load demands. To address this issue, this paper presents an innovative variable-stiffness rotational mechanism designed for use with FSMs. Firstly, the working principle of the variable-stiffness mechanism is introduced, and the influence of the length of each structure on the stiffness and the nonlinear influence are analyzed. Then, the variable-stiffness mechanism is applied to the FSM for the variable-stiffness experiment and variable-load experiment. The experimental results show that the variable-stiffness mechanism designed in this paper can realize the change in stiffness. The errors between the experimental value and the theoretical value of the three sets of experiments are +5.72%, +7.57%, and +6.57%. The FSM's stiffness nonlinearity is very small, and the resonance frequency of the FSM before and after increasing the load can be consistent. The variable-stiffness mechanism can change the frequency characteristics by changing the rotational stiffness of the FSM. [ABSTRACT FROM AUTHOR]