Alfonso J Carrillo, María Balaguer, Cecilia Solís
et al.
Nanoparticle exsolution is a powerful technique for functionalizing redox oxides in energy applications, particularly at high temperatures. It shows promise for solid oxide fuel cells and electrolyzers. However, exsolution of other chemistries like metal oxides is not well studied, and the mechanism is poorly understood. This work explores oxide exsolution in PrBa _1− _x Co _2 O _6− _δ ( x = 0, 0.05, 0.1, 0.15) double perovskites, practiced electrodes in proton ceramic fuel cells and electrolyzers. Oxide exsolution in PrBa _1− _x Co _2 O _6− _δ aimed at boosting the electrocatalytic activity and was evaluated by varying intrinsic materials-related properties, viz. A-site deficiency and external parameters (temperature, under fixed time, and p O _2 = 10 ^−5 atm conditions). The materials were analyzed with conventional characterization tools and synchrotron-based small-angle x-ray scattering. Unlike metal-nanoparticle exsolution, increasing the A-site deficiency did not enhance the extent of oxide-nanoparticle exsolution, whereas larger nanoparticles were obtained by increasing the exsolution temperature. Combined Raman spectroscopy and electron microscopy analysis revealed that BaCoO _3 , Co _3 O _4 , and amorphous BaCO _3 nanoparticles were formed on the surface of the double perovskites after the reductive treatments. The present results demonstrate the complexity of oxide-nanoparticle exsolution in comparison with metal-nanoparticle exsolution. Further materials screening and mechanistic studies are needed to enhance our understanding of this method for functionalizing proton ceramic electrochemical cells (PCEC) electrodes.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
Traditional obstacle avoidance algorithms usually use a single shallow application, such as sensor-based distance measurement or some logic judgment algorithm, which leads to problems such as the need to manually adjust the parameters first, the inability to recognize complex or unknown environments, and the recognition errors caused by significant noise errors. Therefore, to overcome these limitations, this paper combines convolutional neural network and obstacle avoidance algorithms. A model of obstacle avoidance method based on convolutional neural network established in this paper, and puts forward the theory of obstacle avoidance method based on convolutional neural network, which adopts MobileNet_v3 as the learning framework, roughly classifies all the obstacle maps into three categories, and then, through the research and application of six traditional obstacle avoidance algorithms, finally concludes that the model can be applied according to different kinds of obstacles. The model can learn and discriminate against different obstacle maps, thus improving the performance of obstacle avoidance and avoiding the limitations of traditional obstacle avoidance algorithms. Verified the effectiveness of each algorithm in various scenarios. A single shallow application of the problem is usually used to robotize the traditional obstacle avoidance algorithms, which provides an essential reference.
Surface texturing has been proven to be an effective method for improving the lubrication characteristics and tribological behavior of tribo-pairs under various operating conditions. Inspired by the unique Swiss cheese-like leaves of Monstera riedrichsthalii, eight bionic texture patterns were introduced. The influence of vein features, such as costal vein angles (45° and 60°), vein symmetry (symmetric, asymmetric), and elliptical holes, on the tribological and vibration characteristics of rolling bearings was investigated under starved lubrication through a wear test rig and time‒frequency domain vibration signal analysis. The results show that the average coefficients of friction and wear losses of the Monstera riedrichsthalii bionic-textured groups are generally lower than those of the smooth reference. The amplitudes and parameters (i.e., peak value, root mean square (RMS), and crest factor) of the time-domain vibration signals of the textured groups are greater than those of the smooth group in the early stages, but the vibration parameters of most textured groups are lower than those of the smooth bearings in the later stages, especially those of the groups with elliptical holes. The amplitudes and power spectral density (PSD) curves of the frequency-domain vibration signals exhibit similar variations to those of the time-domain signals. Compared with the smooth reference, the Monstera riedrichsthalii bionic-textured group with a combination of 45° secondary-vein angle, asymmetry, and elliptic holes can provide excellent tribological and vibration performance. Its well-lubricating period, average coefficient of friction (CoF), and mass loss can be effectively prolonged or reduced by 37.4%, 7.3%, and 43.9%, respectively.
The Electrochemical Machining (ECM) method is one of the most widely used processing methods in metal surface processing, due to its unique advantages. However, the electrolyte in ECM causes stray corrosion on the workpiece. To overcome these shortcomings, we have developed a no-stray-corrosion ECM method called the controllable electrolyte distribution ECM (CED-ECM) method. However, its practical application in metal surface processing remains largely unexplored. In this study, to improve the CED-ECM method, we delved deeper into the aforementioned aspects by simulating the actual ECM process using COMSOL Multiphysics and rigorously validating the simulation results through practical experimental observations. Then, our efforts led to the application of the CED-ECM method to metal surface processing for the SUS304 workpiece, producing noteworthy results that manifest in diverse cross-sectional profiles on the processed surfaces. This research demonstrates a validated simulation framework for the CED-ECM process and establishes a method for creating user-defined surface profiles by controlling pass intervals, enabling new applications in surface texturing.
Sharon Guardado, Risha Parveen, Zheying Zhang
et al.
The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.
This review article provides a comprehensive examination of the latest advancements in the research and development of Polylactic Acid (PLA) and its composites, with a focus on enhancing material properties and exploring sustainable applications. As a biodegradable and bio-base polymer, PLA has emerged as a promising alternative to conventional petroleum-based plastics across various industries, including packaging, 3D printing, and biomedical fields. The review delves into studies investigating the effects of environmental conditions on PLA’s hydrolytic stability and structural integrity, as well as the benefits of blending PLA with other biopolymers to improve its mechanical properties. It also covers research on optimizing three dimensional printing parameters for PLA, underscoring the importance of raster orientation and print layer thickness in achieving desired mechanical strength and object durability. Additionally, the incorporation of nanofillers and copolymers is discussed as a strategy for enhancing PLA’s moisture resistance and overall performance. By summarizing key findings from a wide range of studies, this article aims to shed light on the significant progress made in PLA research, while pointing out future research directions to resolve existing limitations and fully capitalize on PLA’s potential as a green material solution. To better cater to the needs of design engineers, this review highlights how advancements in PLA research can be directly applied to improve product design and functionality. Specifically, it discusses the enhanced mechanical properties, sustainability benefits, and versatility of PLA in various industrial applications, providing engineers with a deeper understanding of how to utilize PLA in eco-friendly design solutions.
Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy
et al.
In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.
Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi
et al.
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.
Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. This study aims to enhance the practicality of bearing fault diagnosis to meet real-world engineering requirements. In real industrial environments, the continuously changing operating conditions such as equipment speed and load pose challenges in collecting data for bearing fault diagnosis, as it is challenging to gather data for all operational conditions. This paper proposes a transfer learning approach for bearing fault diagnosis based on adaptive batch normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the Case Western Reserve University dataset and Northeast Forestry University (NEFU) dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.
For a long time, the business activities of industrial companies in mechanical engineering, plant construction or the automotive industry focused on products and product-related services. Digital pioneers have refined these products such as machinery, equipment, and devices with data-driven services, which they make available worldwide via digital platforms. This emerging trend influences existing and future business models of all companies. This transition from product-related, single-sided markets to platform markets is characteristic of the digital age. The speed at which business models must change continues to be underestimated by many market participants, especially when order books are well filled and the pressure to change appears to be low. Industrial and service companies need to adapt to the changes induced by new market players to secure future business success and remain competitive in the digital age. The aim of this article is to intensify the debate on digital business models in the industry by providing practical examples of business model innovations in three industries.
To study the process of interaction between the working bodies of soil tillage machines and the soil, it is necessary to create a physical and mathematical model of the environment that reflects the physical and mechanical properties of the real soil as accurately as possible. The existing analytical models are used separately from each other, which leads only to a one-sided consideration of the scientific and technical problem. Today, it is quite difficult for agricultural engineers to investigate the process of interaction of tillage working bodies with the soil during the design of new structures due to the lack of simple analytical physical and mathematical models. In order to simplify these calculations within the framework of agricultural machinery engineering, it is necessary to use software that will combine the achievements of agricultural mechanics. The goal is to simulate and study the process of interaction of tillage working bodies with the soil using Simcenter STAR-CCM+. In the course of the study, the interaction process of the most common tillage working bodies, such as a cultivator’s arrow foot, a disc harrow on an elastic rack, a deep loosener (chisel plow), a flail plow and a smooth roller, was simulated using volume of fluid (VOF) and discrete element methods. (DEM). The application of the VOF method allows to determine the non-primary flow of the soil relative to the working body, and the DEM method allows to determine the distribution of velocities and interaction forces of soil particles. With the help of Simcenter STAR-CCM+, it is possible to visualize the interaction process and determine the height of the ridges formed and the depth of the furrows and their location in space
In the disciplines of industrial machinery, mechanical engineering is beneficial to recognize motor performance for motors with HP power, torque transducer, dynamometer, and control electronics. The motivation is to address the need for more accurate and efficient fault prediction in machinery to prevent breakdowns, reduce maintenance costs, and improve overall reliability. In this work, deep learning classifiers used to classify ball defect inner race fault, outer race fault and normal motor performance in testing. With the aid of three distinct classifiers CNN, FFNN, and RBN; these suggested relative characteristics are assessed. In comparison to other current algorithms, the suggested methodology for classifying motor performance achieved maximum accuracy in each CNN test at 95.4% and 97.7%. The correlation and chi-square algorithms are used to find out the added characteristics and rank of features. The correlation technique provides relations between attributes, and the chi-square offers the optimal balance between precision and feature space. We discovered that the performance is enhanced overall by relative power characteristics. The suggested models might offer rapid responses with less complexity.
ZigBee technology is a low-power, low-cost, short-range wireless communication technology for various IoT (Internet of Things) applications. Machinery parts detection system monitors the quality, size, shape, and other parameters of machinery parts in real time to ensure the accuracy and quality of the production process. In this article, we propose a machinery parts detection scheme based on ZigBee technology. By deploying wireless sensor networks, the detection data concerning machinery parts could be transmitted in real time, thus reducing the complexity of wiring and improving detection efficiency. ZigBee based machinery parts detection system would provide an efficient and convenient means of detection for the manufacturing industry, thus enjoying a wide range of application prospects.
Syuan-Cheng Chang, Chung-Ping Chang, Yung-Cheng Wang
et al.
In this research, we propose a method that utilizes machine learning to maintain the parallelism of the resonant cavity in a Fabry–Perot interferometer designed specifically for glass substrates. Based on the optical principle and theory, we establish a proportional relationship between interference fringes and the inclination angle of the mirrors. This enables an accurate determination of the inclination angle using supervised learning, specifically classification. By training a machine learning model with labeled data, interference fringe patterns are categorized into three levels, with approximately 100 training data available for each level in each location. The experimental results of Level 2 and Level 3 classification indicate an average number of corrections of 2.55 and 3.55 times, respectively, in achieving the target position with a correction error of less than 30 arc seconds. These findings demonstrate the essential nature of this parallelism maintenance technology for the semiconductor industry and precision mechanical engineering.
The mango pulp weevil (MPW) is an aggressive pest that mates seasonally according to the cycle of the mango fruit. After discovering the existence of the mango pulp weevil in Palawan, the island has been under quarantine for exporting mangoes. Detection of the pest proves difficult as the pest does not leave a physical sign that the mango has been damaged. Infested mangoes are wasted as they cannot be sold due to damage. This study serves as a base study for non-invasive mango pulp weevil detection using MATLAB machine learning and audio feature extraction tools. Acoustic sensors were evaluated for best-fit use in the study. The rationale for selecting the acoustic sensors includes local availability and accessibility. Among the three sensors tested, the MEMS sensor had the best result. The data for acoustic frequency are acquired using the selected sensor, which is placed inside a soundproof chamber to minimize the noise and isolate the sound produced by each activity. The identified activity of the adult mango pulp weevil includes walking, resting, and mating. The Mel-frequency cepstral coefficient (MFCC) was used for feature extraction of the recorded audio and training of the SVM classifier. The study achieved 89.81% overall accuracy in characterizing mango pulp weevil activity.