Publikační činnost VŠB-TUO ve Web of Science / Publications of VŠB-TUO in Web of Science

Permanent URI for this collectionhttp://hdl.handle.net/10084/56138

Kolekce obsahuje bibliografické záznamy článků akademických pracovníků VŠB-TUO v časopisech indexovaných ve Web of Science od roku 1990 po současnost. Odkaz na Web of Science je funkční ze sítě VŠB-TUO, vzdálený přístup viz web ÚK VŠB-TUO.

Do kolekce jsou zařazeny:

a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská – Technická univerzita Ostrava (VŠB-TUO),

b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

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Now showing 1 - 20 out of 8151 results

Computation of dynamic deflection in thin elastic beam via symmetries
(Elsevier, 2024) Majeed, Zain; Jhangeer, Adil; Mahomed, F. M.; Zaman, F. D.
The deflection profiles governed by Euler Bernoulli's fourth-order equations under varied applied loads are investigated in this research. This study provides essential insights for engineers designing aircraft components, bridges, and similar structures, ensuring system safety and efficiency. The investigation emphasizes critical factors such as amplitude and frequency, load history, and material properties. Initially, conservation laws of the equations with applied loads are derived by expressing them in the Euler-Lagrange form, where the resultant conservation laws satisfy the divergence expression. The association between symmetries and conservation laws is demonstrated, followed by the application of double reduction theory, which reduces both the variables and the order of the equation. Graphical representations of the outcomes illustrate the impact of load variations on the beam's deflection profiles. These visual aids facilitate a deeper understanding of the influence of different loading conditions. A comparison between varying loads is presented, showcasing the impact of these variations on structural behavior. The findings are crucial for enhancing structural design and ensuring safety under varied loading conditions, showcasing the novelties in the analytical approach and the practical applications of the derived results.
Systemic risk detection using an entropy approach in portfolio selection strategy
(Springer Nature, 2024) Neděla, David; Tichý, Tomáš; Torri, Gabriele
This paper focuses on the investigation and detection of systemic risk. Such risk significantly affects the financial markets and the banking sector, and is fundamental for macro-prudential regulation. To address this issue, we propose an early warning system to anticipate periods of distress. In particular, we consider systemic risk from the investors' perspective, developing optimal portfolio strategies that incorporate such an early warning system based on different entropy measures to predict and hedge the occurrence of systemic risk. On top of this, we introduce a rule that, in periods of crisis, triggers a switch to a risk-free portfolio. In order to determine the optimal composition of a portfolio, we use a new double-optimization strategy, which consists of the maximization of selected performance ratios in the first step and the minimization of selected systemic risk indicators (CoVaR, Marginal expected shortfall) for a given expected return in the second step. An empirical analysis shows that the proposed strategy allows reducing the total risk of the portfolio and generally improves its profitability. We finally discuss how the introduction of these investment strategies may affect the overall stability of the financial system.
Analysis of the computational costs of an evolutionary fuzzy rule-based internet-of-things energy management approach
(Elsevier, 2025) Mikuš, Miroslav; Konečný, Jaromír; Krömer, Pavel; Bančík, Kamil; Konečný, Jiří; Choutka, Jan; Prauzek, Michal
This study presents an in-depth analysis of the computational costs associated with the application of an Evolutionary Fuzzy Rule-based (EFR) energy management system for Internet of Things (IoT) devices. In energy-harvesting IoT nodes, energy management is critical for sustaining long-term operation. The proposed EFR approach integrates fuzzy logic and genetic programming to autonomously control energy consumption based on available resources. The study evaluates the system's computational performance, particularly focusing on processing time, RAM and flash memory usage across various hardware configurations. Different compiler optimization levels and floating-point unit (FPU) settings were also explored, comparing standard and pre-compiled algorithms. The results reveal computational times ranging from 2.43 to 5.23 ms, RAM usage peaking at 6.23 kB, and flash memory consumption between 19 kB and 32 kB. A significant reduction in computational overhead is achieved with optimized compiler settings and hardware FPU, highlighting the feasibility of deploying EFR-based energy management systems in low-power, resource-constrained IoT environments. The findings demonstrate the trade-offs between computational efficiency and energy management, with particular benefits observed in scenarios requiring real-time control in remote and energy-limited environments.
Review of authentication, blockchain, driver ID systems, economic aspects, and communication technologies in DWC for EVs in smart cities applications
(MDPI, 2024) Rajamanickam, Narayanamoorthi; Vishnuram, Pradeep; Abraham, Dominic Savio; Gono, Miroslava; Kačor, Petr; Mlčák, Tomáš
The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a receiver located on the underside of the EV. Dynamic charging offers a solution to the issue of range anxiety by allowing EVs to charge while in motion, thereby reducing the need for frequent stops. This manuscript reviews several pivotal areas critical to the future of EV DWC technology such as authentication techniques, blockchain applications, driver identification systems, economic aspects, and emerging communication technologies. Ensuring secure access to this charging infrastructure requires fast, lightweight authentication systems. Similarly, blockchain technology plays a critical role in enhancing the Internet of Vehicles (IoV) architecture by decentralizing and securing vehicular networks, thus improving privacy, security, and efficiency. Driver identification systems, crucial for EV safety and comfort, are analyzed. Additionally, the economic feasibility and impact of DWC are evaluated, providing essential insights into its potential effects on the EV ecosystem. The paper also emphasizes the need for quick and lightweight authentication systems to ensure secure access to DWC infrastructure and discusses how blockchain technology enhances the efficiency, security, and privacy of IoV networks. The importance of driver identification systems for comfort and safety is evaluated, and an economic study confirms the viability and potential benefits of DWC for the EV ecosystem.
AI-based data mining approach to control the environmental impact of conventional energy technologies
(Elsevier, 2024) Szramowiat-Sala, Katarzyna; Penkala, Roch; Horák, Jiří; Krpec, Kamil; Hopan, František; Ryšavý, Jiří; Borovec, Karel; Górecki, Jerzy
Environmental pollution remains one of the foremost existential threats to human well-being, despite the concerted efforts and implementation of various programmes aimed at fostering cleaner air. The contemporary global economic and energy landscape, characterised by multifaceted challenges, has undeniably hindered the efficacy of efforts to kerb air pollutant emissions. Solid fuels persist as primary sources of energy production in numerous countries, serving both the residential and industrial sectors. However, combustion of such fuels, particularly within domestic heating units (DHUs), engenders the release of a diverse array of organic compounds characterised by intricate structures and potent mutagenic and environmentally hazardous properties. However, the combustion process, if properly regulated, can be carried out in an environmentally sustainable manner. The intricate interplay of myriad factors that influence the composition and quality of chimney flue gases underscores the complexity inherent in controlling the combustion process. Artificial intelligence (AI) has emerged as a versatile tool with applications that span various domains, including environmental monitoring systems. In this study, we posit the utilisation of artificial neural networks (ANNs) as a sophisticated data mining technique to control the emission of flue gases contingent on the specific boiler and fuel utilised. Feed forward predictive models with back propagation were utilized for AI-based data mining aiming at the prediction of the concentration of flue gas components. The highest coefficients of model fit goodness were obtained for CO2, 2 , NOx x and SO2 2 with R2 2 equal to 0.99, 0.98 and 0.99, respectively. The study demonstrated the feasibility and effectiveness of using AI-based data mining to predict emissions from conventional energy technologies. By leveraging the predictive capabilities of ANNs, it is possible to significantly reduce the environmental impact of solid fuel combustion, contributing to cleaner air and improved public health.
Utilization of high-performance concrete mixtures for advanced manufacturing technologies
(MDPI, 2024) Sucharda, Oldřich; Gandel, Radoslav; Ćmiel, Petr; Jeřábek, Jan; Bílek, Vlastimil
The presented experimental program focuses on the design of high-performance dry concrete mixtures, which could find application in advanced manufacturing technologies, for example, additive solutions. The combination of high-performance concrete (HPC) with advanced or additive technologies provides new possibilities for constructing architecturally attractive buildings with high material requirements. The purpose of this study was to develop a dry mixture made from high-performance concrete that could be distributed directly in advanced or additive technologies of solutions in pre-prepared condition with all input materials (except for water) in order to reduce both financial and labor costs. This research specifically aimed to improve the basic strength characteristics-including mechanical (assessed using compressive strength, tensile splitting strength, and flexural strength tests) and durability properties (assessed using tests of resistance to frost, water, and defrosting chemicals)-of hardened mixtures, with partial insight into the rheology of fresh mixtures (consistency as assessed using the slump-flow test). Additionally, the load-bearing capacity of the selected mixtures in the form of specimens with concrete reinforcement was tested using a three-point bending test. A reference mixture with two liquid plasticizers-the first based on polycarboxylate and polyphosphonate and the second based on polyether carboxylate-was modified using a powdered plasticizer based on the polymerization product Glycol to create a dry mixture; the reference mixture was compared with the developed mixtures with respect to the above-mentioned properties. In general, the results show that the replacement of the aforementioned liquid plasticizers by a powdered plasticizer based on the polymerization product Glycol in the given mixtures is effective up to 5% (of the cement content) with regard to the mechanical and durability properties. The presented work provides an overview of the compared characteristics, which will serve as a basis for future research into the development of additive manufacturing technologies in the conditions of the Czech Republic while respecting the principles of sustainable construction.
Mechanical behaviour and energy absorption performance of modified anti-tri chiral novel auxetic structures through experimental and numerical analysis subjected to surrogate metamodeling
(Elsevier, 2026) Ercument, Dervis Baris; Elmoghazy, Yasser Hamed; Al Mahmoud, Zummurd; Safaei, Babak; Sahmani, Saeid; Petrů, Jana
Considering the advanced properties of meta-structures in absorbing energy and high crashworthiness performance, in this work three novel auxetic-lattice structures are proposed and developed based on anti-tri chiral structure by applying the element transformation approach. The novel auxetics were made of polylactic acid plus (PLA+) and additively manufactured by fused deposition modelling. The presented designs were numerically simulated considering elastic-plastic isotropic material properties, using C3D8R brick elements with secondorder accuracy and by implementing mesh size 0.6 mm based on sensitivity analysis through Abaqus/Explicit 2020. The results were further validated by experimental investigation under quasi-static condition subjected to compression load. In addition, a hybrid surrogate metamodel using random forest (RF) and multilayer perceptron (MLP) deep learning has been developed and validated against experimental results. All three novel structures demonstrated auxetic behaviour by achieving negative Poisson's ratio (NPR). The best auxetic characteristics were achieved by the first modified anti-tri chiral (MATC1) structure, with a considerable NPR of -0.42. Furthermore, the numerical simulation results aligned with experimental results and metamodeling prediction has been accomplished. Yet, slightly higher prediction performance was confirmed by RF model over MLP model.
Effect of particulate reinforcement on the aging behavior of aluminium metal matrix composites - A review
(Elsevier, 2026) Kumar, T. Satish; Shalini, S.; Petrů, Jana; Kalita, Kanak
Particulate-reinforced aluminium metal matrix composites (Al-MMCs) have gained renewed attention as promising candidates for aerospace and automotive applications owing to their unique combination of physical and mechanical properties, near-isotropic behavior and cost-effectiveness compared to monolithic alloys. Commercially available Al-MMCs often incorporated with various ceramic particulates, which enhance performance in a range of structural forms. This review examines the influence of particulate reinforcement on the aging behavior of age-hardenable aluminium alloys. The incorporation of ceramic particulates is shown to accelerate the aging kinetics of the matrix, leading to a higher peak hardness. Both increasing the weight fraction and reducing the particle size contribute to greater hardness increments and shorter times to reach peak aging. The underlying mechanisms, including heterogeneous nucleation of precipitates and enhanced dislocation density at particle-matrix interfaces, are discussed to provide a comprehensive understanding of how reinforcement parameters govern aging response in Al-MMCs.
Exploring systems thinking and system dynamics in fire safety engineering: A literature review
(Springer Nature, 2026) Santana, Julio Ariel Duenas; Van Coile, Ruben; Di Benedetto, Almerinda; Salzano, Ernesto
The increasing complexity in the Fire Safety Engineering (FSE) field requires the adoption of risk assessment and safety management methods which address such complex behavior. In this context, complexity refers to systems of multiple interacting components where non-linear and adaptive relationships generate emergent behaviors, i.e., outcomes that cannot be anticipated solely by examining the individual parts. Systems Thinking (ST), including tools such as System Archetypes, Causal Loop Diagrams, and System Dynamics (SD) modeling, offers a holistic framework to address these challenges. This article presents a literature review on the application of ST and SD in FSE, focusing on its use in enhancing fire safety for buildings and infrastructures by identifying key trends, methodologies, challenges, and future research directions. A six-stage framework is adopted for the literature review which examines the development of ST in FSE. In total 35 studies were found as relevant for the FSE field due to their application of at least one ST tool. However, challenges such as the complexity of modeling large-scale systems, the need for high-quality data, and the integration of SD with other fire safety engineering methods remain. Overall, this review underscores the value of ST as a powerful tool for addressing the complexities of FSE, testing the effectiveness of different safety measures, and improving risk assessment in various environments, while highlighting that its potential usage has not been fully developed yet.
A novel diagnostic framework for breast cancer: combining deep learning with mammogram-DBT feature fusion
(Elsevier, 2025) Gupta, Nishu; Kubíček, Jan; Penhaker, Marek; Derawi, Mohammad
Background and motivation: Breast cancer detection remains a critical challenge in medical imaging due to the complexity of tumor features and variability in breast tissue. Conventional mammography struggles with dense tissues, leading to missed diagnoses. Digital Breast Tomosynthesis (DBT) offers improved 3D imaging but brings significant computational burdens. This study proposes a novel framework using the Fully Elman Neural Network (FENN) with feature fusion to enhance the accuracy and reliability of breast cancer diagnosis. Materials and methods: Mammogram images from the CBIS-DDSM dataset and DBT images from the BreastCancer-Screening-DBT dataset were used. The preprocessing step involved Extended-Tuned Adaptive Frost Filtering (Ext-AFF) to enhance image quality by reducing noise. Feature extraction was performed using Disentangled Variational Autoencoder (D-VAE), capturing critical texture features. These features were fused using Deep Generalized Canonical Correlation Analysis (Dg-CCA) to maximize feature correlation across modalities. Finally, a Fully Elman Neural Network was employed for classification, distinguishing between benign, malignant, biopsy-proven cancer, and normal tissues. Results: The proposed FENN-based framework achieved superior classification performance compared to existing methods. Key metrics such as accuracy, sensitivity, specificity, and Matthew's correlation coefficient (MCC) demonstrated significant improvements. The fusion of mammogram and DBT images led to enhanced discriminative power, reducing false positives and negatives across various breast cancer classes. Discussion and conclusion: The integration of mammogram and DBT image data with advanced machine learning techniques, such as D-VAE and FENN, enhances diagnostic precision. The proposed framework shows promise for improving clinical decision-making in breast cancer screening by overcoming the limitations of traditional imaging methods. The system's ability to handle complex interdependencies in imaging data offers substantial potential for earlier and more accurate diagnosis. Future directions: Future research will focus on real-time clinical deployment of the framework, incorporating real-time image acquisition and analysis for faster diagnoses. Additionally, scaling the system for large datasets with varying image quality will further validate its robustness and applicability in diverse clinical environments.
Efficient optimization-based trajectory planning for truck-trailer systems
(MDPI, 2024) Ožana,Štěpán; Krupa, Filip; Slanina, Zdeněk
This paper tackles the complex problem of trajectory planning for trucks with multiple trailers, with a specific focus on autonomous parking assistance applications. These systems aim to autonomously guide vehicles from a starting position to a target location while effectively navigating real-world obstacles. We propose a novel six-phase approach that combines global and local optimization techniques, enabling the efficient and accurate generation of reference trajectories. Our method is validated in a case study involving a truck with two trailers, illustrating its capability to handle intricate parking scenarios requiring precise obstacle avoidance and high maneuverability. Results demonstrate that the proposed strategy significantly improves trajectory planning efficiency and robustness in challenging environments.
High performance MXene/MnCo2O4 supercapacitor device for powering small robotics
(American Chemical Society, 2024) Shinde, Nanasaheb M.; Pumera, Martin
The development of advanced energy storage devices is critical for various applications including robotics and portable electronics. The energy storage field faces significant challenges in designing devices that can operate effectively for extended periods while maintaining exceptional electrochemical performance. Supercapacitors, which bridge the gap between batteries and conventional capacitors, offer a promising solution due to their high power density and rapid charge-discharge capabilities. This study focuses on the fabrication and evaluation of a MXene/MnCo2O4 nanocomposite supercapacitor electrode using a simple and cost-effective electrodeposition method on a copper substrate. The MXene/MnCo2O4 nanocomposite exhibits superior electrochemical properties, including a specific capacitance of 668 F g(-1), high energy density (35 Wh kg(-1)), and excellent cycling stability (94.6% retention over 5000 cycles). The combination of MXene and MnCo2O4 enhances the redox activity, electronic conductivity, and structural integrity of the electrode. An asymmetric supercapacitor device, incorporating MXene/MnCo2O4 as the positive electrode and Bi2O3 as the negative electrode, demonstrates remarkable performance in powering small robotics and small electronics. This work underscores the potential of MXene-based nanocomposites for high-performance supercapacitor applications, paving the way for future advancements in energy storage technologies.
Internal friction and flowability of clay powder depend on particle moisture, size and normal stress
(Elsevier, 2024) Prokeš, Rostislav; Jezerská, Lucie; Gelnar, Daniel; Zegzulka, Jiří; Žídek, Martin
This work investigates the impact of changes in the moisture content of bulk materials. The regularity of moisture change in bulk materials was evaluated for three different particle size classes of clay powders. The research measures the angle of internal friction and flowability under four different normal loads, reflecting the varying pressures during bulk material storage. The influence of moisture change in bulk materials was most pronounced for the smallest particle size fraction, where even a very small moisture change in the order of tenths of a percent steeply affected flowability due to internal friction. After increasing the moisture content by a few percent, a steady state of flow occurred. Critical value was determined when water in the bulk material caused liquefaction. For larger particle size fractions, the impact of moisture change was evident only at higher values (12.5%), with no liquefaction occurring even at 30% moisture. The change in normal load, on the other hand, affected particles of larger size fractions, resulting in improved flow properties.
Incidence of poverty in working-age population in EU countries: A gender perspective
(Vysoká škola ekonomická v Praze, 2024) Kovářová, Eva; Vašenková, Tereza
Poverty reduction has long been one of the political priorities of the European Union and its member states. Despite the political declarations and measures applied, poverty is still a phenomenon that affects the everyday lives of about 70 million Europeans. Moreover, trends in poverty incidence show how poverty risks are sensitive to overall socio-economic development and how they are more actual for some vulnerable population groups. Following the popular concept of poverty feminization, the analysis presented in the paper aims to identify gender perspective relationship between the poverty incidence and characteristics describing the situation on the labour market or the levels of attained education in EU-27 countries. Attention is paid to poverty incidence among women and men of working age (population aged from 25 to 54 years) and differences are examined in the relationship to the position of both genders on the labour market. Presented findings, based mainly on the results obtained from the panel regression analysis performed for the period 2007-2020, suggest that policymakers should integrate a gender perspective into all policies focused on poverty reduction.
Comparative study of SFPE and steering modes in Pathfinder to optimise evacuation routes
(MDPI, 2024) Snohová, Adéla; Kučera, Petr
This paper investigates the possibilities of using an agent-based evacuation model to analyse the formation of queues at the exits of enclosed spaces and the evacuation process of people. The aim is to investigate how the density of people in a crowd affects the safe movement of people and how the width, number, and location of exits affect evacuation time. The analysis was carried out using the Pathfinder evacuation model, which provides two modes to simulate the movement of people: steering and SFPE. An enclosed space of 20 m x 30 m was investigated. First, the differences between the modes of the evacuation model to simulate the movement of people were compared. Then, the steering mode with limited door flow was set and the effect of the width, number, and location of the exits with a total width of 4 m on the evacuation time was investigated. The results of this study highlight differences in the simulation of the movement of people according to the different modes and provide valuable information for the design of safe escape routes. Proper design of escape routes can prevent an adverse situation that could arise when evacuating a large number of people.
Verification of numerical models of high thin-walled cold-formed steel purlins
(MDPI, 2024) Pařenica, Přemysl; Krejsa, Martin; Brožovský, Jiří; Lehner, Petr
High thin-walled cold-formed steel purlins of the Z cross section are important elements of large-span steel structures in the construction industry. The present numerical study uses the finite element method to analyse the 300 mm and 350 mm high Z cross sections in-depth. The prepared numerical models are verified and validated at several levels with experiments that have been previously published. Significant agreement between the numerical models and the experimental results regarding Mises stress, proportional strain, failure mode, and force-deformation diagram have been obtained. With the verification, the presented procedure and partial findings can be applied to other similar problems. The results can be used to help research and corporate groups optimise the structural design of cold-formed thin-walled steel structures.
Cigarette taxation and consumption in The Czech Republic. Have these factors influenced tax revenue?
(University of Information Technology and Management in Rzeszow, 2024) Krajňák, Michal
The article evaluates the development of the tax burden on cigarettes in the Czech Republic from 1993 to the end of 2023. Tax burden is represented by effective tax rate. The research results show that the tax burden on these products is still increasing. Not only does the tax burden increase, but so does the price of these products. The regression analysis results show that quantities such as the price of cigarettes, the effective tax rate or the amount of cigarettes consumed are factories that positively influence tax revenue. Since the tax rate or the price of these products constantly increases, the tax revenue is also increasing. It increased almost six- fold over the entire analyzed period. At the same time, it was found that other factors, such as the average wage, also affect the tax revenue. It is necessary to consider that the cigarette consumption has been showing a downward trend in recent years. However, the reason is not only the increasing tax burden and the price of these products but also the transition of consumers to so-called e-cigarettes. A reduction in cigarette consumption may be reflected in the future by reducing healthcare costs, which will create potential reductions in public health insurance payments.
Stochastic dynamics and control in nonlinear waves with Darboux transformations, quasi-periodic behavior, and noise-induced transitions
(MDPI, 2026) Jhangeer, Adil; Imran, Mudassar
Stochastically forced nonlinear wave systems are commonly associated with complex dynamical behavior, although little is known about the general interaction of nonlinear dispersion, irrational forcing frequencies, and multiplicative noise. To fill this gap, we consider a generalized stochastic SIdV equation and examine the effects of deterministic and stochastic influences on the long-term behavior of the equation. The PDE was modeled using a stochastic traveling-wave transformation that simplifies it into a planar system, which was studied using Darboux-seeded constructions, Poincar & eacute; maps, bifurcation patterns, Lyapunov exponents, recurrence plots, and sensitivity diagnostics. We discovered that natural, implicit, and unique seeds produce highly diverse transformed wave fields exhibiting both irrational and golden-ratio forcing, controlling the transition from quasi-periodicity to chaos. Stochastic perturbation is demonstrated to suppress as well as to amplify chaotic states, based on noise levels, altering attractor geometry, predictability, and multistability. Meanwhile, OGY control is demonstrated to be able to stabilize chosen unstable periodic orbits of the double-well regime. A stochastic bifurcation analysis was performed with respect to noise strength sigma, revealing that the attractor structure of the system remains robust under stochastic excitation, with noise inducing only bounded fluctuations rather than qualitative dynamical transitions within the investigated parameter regime. These findings demonstrate that the emergence, deformation, and controllability of complex oscillatory patterns of stochastic nonlinear wave models are jointly controlled by nonlinear structure, external forcing, and noise.
High-temperature cyclic oxidation and microstructural behavior of CoMoCrSi-based composite coatings with Al2O3 and YSZ on T91 steel
(Elsevier, 2026) Shetty, Rakshith Kumar; Hebbale, Ajit M.; Chandramouli, T. V.; Ramesh, M. R.; Petrů, Jana
This investigation focuses on the cyclic oxidation behaviour and microstructural evolution of CobaltMolybdenum-Chromium-Silicon coatings reinforced with alumina and yttria stabilised zirconia (YSZ) deposited on T91 steel by the atmospheric plasma spraying method. Characterization of the as-sprayed coatings was done to provide a consistent base line for comparison of the intercoatings. Cyclic oxidation experiments at 800 degrees C for 50 oxidation cycles was carried out with surface and cross section analyses using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) for the evaluation of oxidation scale formation, element distribution and phase stability. After cyclic oxidation, XRD showed an appearance of stable oxide constituents, which demonstrates the formation of protective scales on the coating surfaces. In comparison with unreinforced and alumina reinforced coatings, the YSZ reinforced coating displayed comparatively superior oxidation behavior as reflected in the more stable oxidation and less degradation during cyclic exposure. SEM examination of oxidized surfaces showed that the ceramic additions helped to increase coating integrity, improve scale adherence and reduce oxygen penetration. These effects were amplified for the YSZ-containing coating, showing a good response for high temperature oxidation. Overall, the addition of ceramic reinforcements, especially of YSZ, improved the phase stability and oxidation resistance of the CoMoCrSi-based coatings, which supports the suitability of the coatings for demanding boiler environments.
Mechanical characteristics and failure analysis of AA6082/TiC/graphene hybrid composites
(IOP Publishing, 2026) Lal, Sohan; Mittal, Rashmi; Sharma, Neeraj; Čep, Robert; Kumar, Ajay
The main aim of the present research is to improve the tensile strength of lightweight aluminium alloy 6082, which makes it fit for many advanced engineering applications. Considerable efforts have been made to investigate the tensile characteristics, hardness, microstructural and fractography of AA6082/TiC/graphene composite developed at different reinforcements loadings varying from 1 wt.% to 6 wt. %. The reinforcements used in the current research are TiC and graphene. The aluminium matrix composite fabricated using nanosized particles by stir-casting route. The tensile strength increases with the increase of TiC and graphene particles in the AA6082 matrix up to certain limit i.e. 5 wt.%. Once the reinforcement increases beyond the point at which agglomeration of the particulates starts, the tensile strength and hardness of the developed composites decrease. The x-ray diffraction (XRD) and scanning electron microscopy (SEM) was used for the investigation of elements and morphology. The fractography of the developed composite depicts the morphology of the fractured tensile specimens. The elongated dimples, tear ridges and micro-voids are found during fractography. The failure of the hybrid composites changes from ductile fracture to cleavage failure.

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