Engine Components Research Articles

Generalized composite multiscale improved diversity entropy and its application to diesel injector fault feature extraction and diagnosis

The diesel injector is the core component of marine diesel engines, and its health monitoring is one of the keys to ensuring the efficient and reliable operation of diesel engines and even ships. However, extracting effective feature information from nonlinear dynamic signals is the difficulty in realizing intelligent fault diagnosis of diesel fuel injectors. The existing diversity entropy (DE) is an advanced algorithm for extracting fault features. Still, it has the problem of failing to obtain sufficiently effective fault features, thus affecting diagnostic accuracy due to its inherent defects. To solve the above problem, a new generalized composite multiscale improved diversity entropy (GCMIDE) is first proposed, and the comparative advantages of the proposed entropy in terms of consistency, robustness, and computational efficiency are verified with common noise-simulated signals. GCMIDE is further utilized as an excellent means of feature extraction, and a new injector fault diagnosis method, GCMIDE-Relieff-SSA-BP, is proposed to meet the needs of efficiency and practicality in fault diagnosis. Finally, the method is adopted for the diagnostic experiments of diesel fuel injectors with different fault types and different fault degrees for full validation. The experimental results all indicate that, in comparison with six commonly used entropy-based fault diagnosis methods, the proposed method has the best feature extraction performance and fault diagnosis accuracy under a different number of features or different training sample ratios. This provides an effective tool for fault diagnosis of injectors and even intelligent operation and maintenance of diesel engines.

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

SMART-STRATEGIES AND INVESTMENT ATTRACTIVENESS OF THE EU REGIONS

The purpose of the article is to generalize the foundations of formation of smart-strategy of country / region development as a factor of impact on the level of investment attractiveness. The development of globalization processes has led to positive consequences, resulting in the growth of the world’s economy. At the same time, the competition in the sphere of attracting investment resources has aggravated. Based on the analysis of indicators of investment activity of European countries and the investment attractiveness of the EU countries, it was found that the specified area is characterized by the presence of negative trends, which actualizes the need to develop tools for increasing the level of investment attractiveness. The system of factors of impact on the level of investment attractiveness of the country / region was studied. It was found that in current conditions, in addition to the traditional factors of influence on investment attractiveness, intellectual potential, flexibility, education and innovation, networking, etc. are added, which serves as the basis for the implementation of smart approaches in the formation of regional development strategies. The article defines the features of the formation of a smart approach to development strategies, which is based on the combination of technological and industrial components. It was found that the basis of smart-strategies is the specialization of the country / region, which is formed considering the resource provision and development potential of the region, the ability to diversify, the ability to achieve a high level of concentration of business entities in the region, the place of the region in the economic system. Building a regional policy based on smart-strategies will stimulate economic development, increase competitiveness and investment attractiveness of countries / regions. On the basis of the conducted research, a number of challenges related to the implementation of the smart approach were identified, namely, a high level of subjectivism and the need to ensure a high level of qualification of the participants in the discussions when implementing the mechanism of inclusive management; the growth of internal competition in the case of the formation of homogeneous specialization of several EU countries / regions; taking into account external risks, one of which is a full-scale Russian-Ukrainian war; implementation of adaptive approaches in the process of formation and implementation of smart-strategies. Addressing these challenges will contribute to increasing the efficiency of smart-strategy formation for a high level of investment attractiveness of the EU regions.

Estimation of the Remaining Useful Life of Axially Loaded Members Through Nested Least-Square Support Vector Regression

ABSTRACT Prognosis and Health Management (PHM) is crucial for ensuring the reliable operation of structural components, such as bracing members. Despite this, limited study exists for the RUL estimation of civil engineering components. This paper presents a nested framework utilizing the Least Square Support Vector Regression (LS-SVR) for accurately predicting the Remaining Useful Life (RUL) of axially loaded members. The framework addresses the challenges of sparse data and multidimensional damage-sensitive features (DSFs) by employing a two-stage process. In the first stage, DSFs are forecasted using one-dimensional mapping, providing essential inputs for the second stage, where axial stiffness degradation is predicted using LS-SVR. The proposed framework is validated through an experimental study on axially loaded members subjected to low-cycle fatigue. To enhance model performance, simulated data generated from an OpenSees model is combined with the experimental data. The nested LS-SVR framework demonstrates superior performance compared to traditional methods, achieving high accuracy in RUL estimation as evidenced by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The framework’s ability to forecast axial stiffness degradation, a critical indicator of structural health, makes it a valuable tool for real-time assessment of structural component RUL. By integrating this framework with real-time monitoring systems, proactive maintenance strategies can be implemented, leading to safer and more efficient structural health management.

Cloud based adaptive learning system: virtual reality and augmented reality assisted educational pedagogy development on clinical simulation

An adaptive learning system provides support for student’s academic progress by varying learning routes and pedagogical contents to satisfy various students’ unique academic needs. The primary goal of this research work is to enhance and expand occupational efficiency through the use of technological pedagogical content knowledge (TPCK) frameworks in nursing education. The authors developed an adaptive federated cloud computing learning system in order to better prepare students for the demands of real-world professional practice. The article examined the use of virtual reality(VR) and augmented (AR) technologies in nursing education and developed an instrument that skillfully combines knowledge from the reputable literature on clinical competencies with the peculiarities and ingenuousness particular to virtual simulations. Designated for the remote sharing and utilization of combined educational resources, cloud computing is a distributed information communication technology (ICT) infrastructure built on the Internet of Things (IoT) framework. To increase the occupational efficiency among the nurses and midwives, an educational simulation applied in a way that gamifies the curriculum component for interactive educational pedagogy will be necessary. As a completely integrated part of the curriculum, simulation has proven to be beneficial for nursing students at all levels of instruction. Nursing educators have a plethora of options at their disposal to introduce simulation-based learning approaches into their curricula in the modern era. A three-dimensional (3D) interactive environment that provides users with a sense of spatial presence is created through computer technology, which is an immersive digital ecosystem of VR and AR utilized to improve teaching and learning in nursing education. In this study, the authors offered a cost- effective clinical simulation laboratory for clinical students, nurses, midwives, and practicing healthcare professionals in a manner that ensures constant learning and occupational efficiency by adopting the TPCK model in its central discussion and implementation. The TPCK model has demonstrated how to recognize the boundaries between technological components and pedagogical structures as well as how they are connected when creating facts in nursing education.

Experimental investigation on short-time and long-time tribological performance of Fischer-Tropsch fuel compared with fossil diesel

ABSTRACT The Fischer-Tropsch (F-T) fuel, as an alternative fuel for internal combustion engines, can cause undesired wear issues on engine components mainly due to its almost zero sulfur content. In order to explore a conveniently accessible additive to improve the lubricity of the F-T fuel, this study conducted both short-time and long-time tribological performance experiments of the F-T fuel added with 200 ppm lubrication improver Infineum R655 and compared with commercial fossil diesel. A four-ball wear test machine was used to perform fuel anti-wear and load-carrying properties tests. Two four-cylinder fuel pumps with top clearance and flat top two types of plungers were, respectively, operated with the F-T fuel and diesel for 300 hours. The parameters characterizing the tribological performance of the fuel pump plunger couples before and after the durability test were measured, including roundness, straightness, depth of parallelism, line profile, sealing time, and fuel delivery amount. The anti-wear property test results show that the wear scar diameter of the metal ball is 0.59 mm for the F-T fuel, which is 7.8% decreased than that of diesel. The results of fuel load-carrying capacity experiment exhibit that the maximum nonseizure load of the F-T fuel is 246 N, which is better than that of diesel. Regarding the fuel pump bench 300-hour operating results, wear of the fuel pump plunger components was acceptable using the F-T fuel with the lubrication improver, without any modification of fuel pumps. Additionally, the improvement brought by this additive is applicable to the two common types of plunger components mentioned above. The results of this study reveal that the desired tribological performance of the F-T fuel can be achieved by adding the lubrication improver additive evaluated in this work.

Isolating influences of varying pitch from the effects of non-zero compound angles on effusion cooling

Abstract Effusion cooling is the state-of-the-art cooling technology for gas turbine hot-gas path components. Typically, effusion cooling holes across the entire combustor liner are aligned with the combustor axis, rendering a nominal zero compound angle between highly directional miniature effusion cooling jets and the main flow direction. The pitch of effusion cooling holes is optimised accordingly. However, the swirling main flow results in a non-zero compound angle and an effectively different pitch from the design. The directional effect of effusion cooling as a result of swirling main flow on the adiabatic film cooling effectiveness (AFE) is a combined effect of a non-zero compound angle and a varied pitch. The current experimental study aims to investigate the isolated effects of compound angle on AFE by excluding the influences of varying pitch. With an improved understanding of the sole effects of non-zero compound angles on AFE, the roles that a varied pitch plays in modifying AFE are further discussed to guide future effusion cooling designs under swirling main flow conditions. Binary pressure sensitive paint (PSP) was used to determine AFE experimentally.

The Sociotechnical Stack: Opportunities for Social Computing Research in Non-Consensual Intimate Media

Non-consensual intimate media (NCIM) involves sharing intimate content without the depicted person's consent, including 'revenge porn' and sexually explicit deepfakes. While NCIM has received attention in legal, psychological, and communication fields over the past decade, it is not sufficiently addressed in computing scholarship. This paper addresses this gap by linking NCIM harms to the specific technological components that facilitate them. We introduce the sociotechnical stack , a conceptual framework designed to map the technical stack to its corresponding social impacts. The sociotechnical stack allows us to analyze sociotechnical problems like NCIM, and points toward opportunities for computing research. We propose a research roadmap for computing and social computing communities to deter NCIM perpetration and support victim-survivors through building and rebuilding technologies.

Science and Innovation Management: Sustainable Development Goals 8 and 9 as Challenges for Poland

This manuscript continues the series of research publications on Sustainable Development Goals 8 and 9 (SDGs 8 and 9). Innovations play an essential role in the global and national economies, as they contribute to the growth of economic productivity, the creation of new products, services, and jobs, and improve the quality of life. In the context of innovation, economy and artificial intelligence (AI) are the ideological and technological components of the innovation process. The authors hypothesized that the Polish system for assessing the importance of scientific economic journals is ineffective for social development and for creating an innovation-based economy. The research methods are a scientometric analysis of 795,070 scientific sources by the keywords “Economy” and “Artificial Intelligence”, a bibliometric analysis of 219,739 sources by the keywords “Economy”, “Economic productivity”, and “Innovation”, correlation analysis, and testing statistical hypotheses. The study led to the adoption of an alternative statistical hypothesis, which is a strong confirmation of the basic hypothesis. A new model for assessing the weight of scientific journals was created based on the existing system. Two management recommendations were formulated in the rules for determining the weight of journals. The high R2 value indicates that the new model effectively predicts the result. The correlation coefficient of the weight of journals in the new model is more than 98%. When verifying statistical hypotheses, a high significance level of 99.0 was used. The study’s practical significance is in the movement of Polish science to a leading place in the world market of scientific products.

A comprehensive cost mapping of digital technologies in greenhouses

Conventional greenhouse producers face significant challenges in integrating advanced Industry 4.0 technologies into their production processes. One of the main obstacles is the lack of clarity regarding the components of technological costs. This article develops a cost mapping for the implementation of such technologies in the context of greenhouses. The mapping distinguishes between capital expenditures (CAPEX) and operational expenditures (OPEX), categorizing the key technological components and their financial implications. Based on the general findings from the literature review, several cost areas can be identified and classified as follows: hardware acquisition, installation and retrofitting, integration and customization, software and services, operational and maintenance costs. This cost structure will serve as a basis for future economic models and cost-benefit analysis (CBA), promoting strategic decision-making and a more informed and precise selection of digital technologies.

Investigation of carbon-black emissions of a tractor biofuel diesel

BACKGROUND: On the one hand, a petroleum-based liquid fuel diesel engine is a reliable basis for tractors and self-propelled agricultural machines, and on the other hand, the current trends make us to think about the environmental component of these diesel engines and besides to remember about reasonable use of non-renewable petroleum motor fuel. In order to reduce the anthropogenic impact on natural ecosystems and to assess the smokiness of exhaust gases from a tractor diesel running on ethanol and rapeseed oil, the paper considers the relevant model of the carbon-black formation inside it. AIM: Development of the relevant model of carbon-black emission in a tractor diesel running on ethanol and rapeseed oil to assess the smokiness of exhaust gases and to reduce anthropogenic impact on natural ecosystems. METHODS: To simulate the processes of formation and burnout of carbon-black particles in a tractor diesel engine, the volume of the combustion chamber was conditionally divided into several areas (soot content indicators in different areas were added up), and the cycle of calculating the exhaust gas smokiness level included several stages (determination of pressure, integral and differential characteristics of heat dissipation, average temperature of the working fluid, fuel supply indicators and fuel evaporation rate, local coefficients of excess air, composition of gases, concentration of decomposition and oxidation products of rapeseed oil and ethanol, the number of carbon-black particles, the mass of dispersed carbon, the rate of transition of particles to the burnout zone). RESULTS: The developed mathematical model is capable of calculating the carbon-black concentration and the main components of the gas mixture in the reaction zone of the combustion chamber, the content of carbon-black in the exhaust gases at various speed and load modes of operation of a tractor diesel engine. It is capable of obtaining valuable information about the dynamics of the main stages of carbon-black formation and burnout in the cylinder of a tractor diesel engine running on ethanol and rapeseed oil. The results of numerical simulation of carbon-black formation and burnout in a tractor diesel cylinder when running on diesel fuel, ethanol and rapeseed oil are obtained and presented. CONCLUSION: Based on the developed relevant model of carbon-black emission in a tractor diesel engine running on ethanol and rapeseed oil, an assessment of its exhaust gas smokiness was carried out, clearly showing a decrease by 3.4–3.8 times in comparison with diesel fuel operation. The presented method for calculating the carbon-black emission of tractor diesel can be used in the multi-area modeling and researches of such in-cylinder processes as heat generation, heat transfer, etc. The accuracy of calculations based on the proposed model is characterized by the perfection of mathematical algorithms describing the rate of fuel evaporation, the development of a fuel flare, the determination of local temperatures, the rate of flame propagation, the local composition of gases in the cylinder, etc.

Examining mechanics problem-solving strategies among engineering students at a selected public university in Rwanda

Abstract This paper presents the findings of research conducted at a selected university in Rwanda aiming to evaluate the mechanics of problem-solving strategies among engineering students. Problem-solving is an important component of science, technology, engineering, and mathematics education as well as in everyday life. To enhance the development of problem-solving skills, researchers and curriculum creators must develop problem-solving strategies for solving mechanics problems and for improving engineering students’ performance in physics classroom tasks. The study investigated engineering students’ strategies for their mechanics problem-solving using a mixed-methods research approach that includes questionnaires, interviews, and tests. A purposive sample of 99 students was chosen, with 20 students and three instructors randomly selected for interviews. Findings revealed that students heavily rely on algebraic equations, neglecting visual representations in mechanics problem-solving. Challenges identified include limited familiarity with alternative representations, difficulties transitioning between them, and a constrained understanding of their interrelationships. Recognizing these practices and strategies informs the design of more effective pedagogical interventions. Incorporating the best problem-solving strategies in physics education is a potential avenue to bolster problem-solving skills and deepen comprehension of mechanics concepts among engineering students.

Quantum Entanglement an Indispensable Tool of Quantum Teleportation

One of the most crucial procedures in quantum information is quantum teleportation. Quantum teleportation, which makes use of the physical resource of entanglement, is a fundamental primitive in many quantum information tasks and is a crucial component of quantum technologies. It is essential to the ongoing development of quantum networks, quantum computing, and quantum communication. Here, the practical applications of Bell states, algebra of entanglement, and quantum dense coding are highlighted. The fundamentals of quantum entanglement are described, along with its characteristics and the Einstein-Poldolsky-Rosen dilemma. By separating its distinctive properties, this paper demonstrated that teleportation will be used practically for quantum key distribution in the very near future. It also revealed the fundamental theoretical concepts behind quantum teleportation.

Integrated electro-optic modulator on a lead zirconate titanate-silicon nitride heterogeneous platform.

Integrated electro-optic (EO) modulators are the core components of the optoelectronic information technology, and lithium niobate is currently the most widely used crystalline thin film material; however, finite EO coefficients limit the modulation efficiency of the modulators. In this Letter, we present an integrated EO modulator using a microring resonator on the lead zirconate titanate (PZT) and silicon nitride (SiN) heterogeneous platform. The microwave attenuation is reduced by using low loss tangent and dielectric constant SiN as the electrode substrate, achieving an EO bandwidth of 33 GHz. Thanks to the high quality of the PZT film deposition and the substantial EO overlap of our structure, ultrahigh modulation efficiency with the half-wave voltage-length product of 0.7 V·cm is achieved. In addition, as a remarkable result, an 80-Gbps on-off keying signal is generated using the modulator.

ROBUST DESIGN OPTIMIZATION OF A COMPRESSOR ROTOR USING RECURSIVE COKRIGING BASED MULTI-FIDELITY UNCERTAINTY QUANTIFICATION AND MULTI-FIDELITY OPTIMIZATION

Abstract This work focuses on the application of multi-fidelity methods for the robust design optimization of engine components. The robust design optimization approach yields geometric designs with high efficiency and low sensitivity to uncertainties from manufacturing and wear. The uncertainty quantification techniques required to evaluate the robustness are computationally expensive, which limits their use in robust optimization. Multi-fidelity methods offer a promising solution to reduce the computational cost while maintaining the accuracy in both uncertainty quantification and optimization. A Kriging and a multi-fidelity recursive Cokriging framework are developed and applied to a test function. Furthermore, a multi-fidelity super efficient global optimization algorithm is developed. The optimizer is surrogate based and handles constraints. The developed methods are then applied to a compressor test case of a high pressure compressor blade row with 9 uncertainty and 24 design parameters of the geometry. The 2.5 % quantile of the stage efficiency is used as a robustness measure and is optimized. Design bounds and performance constraints are applied. Various uncertainty quantification techniques are analyzed. A multi-fidelity uncertainty quantification approach is developed that combines simplified coarse-grid low-fidelity with high-fidelity results to reduce the computational cost while maintaining a high accuracy. Uncertainty quantification techniques of three fidelity levels are developed and used for the multi-fidelity approach in the design space. The robust design optimization is performed and the optimal designs obtained from the multi-fidelity approach show superior performance compared to existing robust design optima in the literature.

Modelling project activities for future teachers within the innovative infrastructure of pedagogical universities: integrative solutions

Introduction. In the quest to enhance the training of future teachers, particular emphasis is placed on identifying the educational potential of new learning environments within pedagogical universities under the Ministry of Education of Russia. These institutions have undergone infrastructure updates as part of federal programs designed to support education. Aim. The aim of this study is to develop an original model for organising the project activities of student teachers, taking into account the contemporary infrastructural capabilities of pedagogical universities under the Ministry of Education of Russia. Methodology, methods and techniques. The methodological foundation of this research is grounded in the principles of community development pedagogy and the design of evolving educational systems. The integration of integrative, synergetic, environmental, and system-activity approaches facilitated the identification and development of components within the author’s model for organising project activities for future teachers in the context of pedagogical technoparks, such as “Quantorium”, and technoparks focused on universal pedagogical competencies in higher education institutions under the Ministry of Education of the Russian Federation. The validation of the developed model was conducted through expert assessments. Results. The authors developed the model for organising project activities for future teachers within the innovative infrastructure of pedagogical universities. The main components of the model are outlined, including integrative objectives and anticipated outcomes of students’ project activities, interdisciplinary project content, and a technological component that facilitates the transformation of interdisciplinary project content into personally meaningful experiences through the technology of collaborative distributed project activities. The study identifies and justifies the conditions necessary for the effective implementation of the developed model, focusing on the management of processes, resources, and participants involved in interdisciplinary project activities. Scientific novelty. Integrative processes, within the context of this study, are regarded as a pedagogical phenomenon that reflects the interconnections among the objects, processes, and subjects involved in project activities. Practical significance. The research materials can be utilised in the development of programmes aimed at enhancing the infrastructure of pedagogical universities. They can also assist in the refinement of educational curricula for training teaching staff, particularly in disciplines that prepare future educators to effectively organise project-based activities within contemporary educational environments.

Advanced cryopreservation as an emergent and convergent technological platform

Advanced cryopreservation technologies have the potential to transform organ transplants, biomedical research, food storage, aquaculture, biodiversity repositories, ecological restoration, and numerous other applications. These surpass the capability of existing cryopreservation technologies to extend the life and viability of biological materials at various scales from cells to tissues, organs, and entire organisms. In this article, we demonstrate why innovations in advanced cryopreservation, which we analyze as emergent, convergent platform technologies, raise novel concerns for research ethics and coordination, governance, and equitable access to benefits. As emerging technologies, they may disrupt markets or destabilize social institutions, including the systems that govern the distribution of organs for transplant. As convergent technologies, their impact will be heightened through interaction with other technologies. The technologies that may intensify the social and ethical effects of advanced cryopreservation include information technologies that permit the administration of complex logistics of storage and transport, biotechnologies for the management of floral and faunal species and populations, and 3D printing technologies that may enable the development and distribution of customizable peripheral components of this platform technology. The speed of development among diverse applications of the core platform is likely to vary between sectors in ways that are responsive to public support as well as to ethical constraints, and advancements in any sector will affect the achievement of reliability for the core technology across sectors. We recommend that societal benefits and risks be assessed both in the specific contexts for which peripheral components are developed and for the core technology.

Numerical simulation of micro gas turbine engine based on time-marching one-dimensional method

The advancement of gas turbine technology serves as a crucial indicator of high-tech and technological prowess, with widespread applications in aviation, marine vessels, power generation, and clean energy. The precision of the performance simulation model stands as a pivotal component in the development of gas turbines. The present study deals with the one-dimensional modeling and validation of gas turbines by utilizing a time-marching scheme, in order to establish the correlation between the engine components and full-engine performance. The flow through the turbomachinery was solved by the one-dimensional model. The combustor was modelled as a compressible actuator disk. The accuracy of the turbomachinery solver was validated using the KJ66 compressor and turbine, and the predicted results demonstrated consistency with the experimental data or the three-dimensional simulations. The characteristic interface method was developed based on Riemann invariants, in order to provide the flow properties on both sides of the combustor. The results indicated that compared to the component-by-component approach, the developed characteristic interface method significantly reduced the computational time. Finally, the developed one-dimensional method was employed to simulate the performance of the KJ66 micro gas turbine engine. The predicted performance for the KJ66 gas turbine engine was consistent with the trend in the experiment results. The novelty of the present study was its modeling approach, and its ability to eliminate the dependence on the general component map, and obtain meridional distributions of flow parameters, which cannot be achieved in traditional zero-dimensional thermal cycle calculations. These results can provide an accurate and powerful tool for the analysis and optimization of component and full-engine performance in the preliminary design stage.

Study on the Mechanical Properties of beta Silicon Nitride Based on Neural Network Potential

Silicon nitride is widely utilized in the manufacture of cutting tools, bearings, and engine components, making accurate predictions of its mechanical and thermal behaviors crucial for engineering design. This study significantly advances the modeling of complex atomic interactions by employing machine learning, specifically Neural Network Potential (NNP), to enhance precision over traditional empirical potentials. Through NNP, predictions of stress-strain curves, thermal conductivity, and grain boundary behavior are achieved. The results demonstrate that NNP not only reduces computational time but also enhances the accuracy of material property predictions. This work provides the mechanical and thermal performance of Si3N4 and opens new avenues for further applications of machine learning in advanced ceramic materials.

Combined hybrid machining of laser ablation-drilling small holes in Cf/SiC composites

Carbon fiber-reinforced silicon carbide composite material (Cf/SiC) serves as a typical advanced material for fabricating hot-end components of aircraft engines. Its unique properties, including high hardness and anisotropy, pose significant challenges in processing. Nevertheless, a crucial structural element of hot-end components, specifically the cooling and heat dissipation holes, necessitates hole machining within the Cf/SiC composites. The intricate nature of machining this material often leads to compromised surface quality and severe wear on cutting tools during the hole machining process. This study delves into the feasibility of combined hybrid machining of laser ablation-drilling(L-DCM) for fabricating small holes in woven Cf/SiC composites, along with elucidating the underlying material removal mechanisms. The findings reveal approach significantly enhances the cutting performance of hard alloys tools when dealing with ceramic matrix composites. Notably, the wear mechanisms of the cutting tools primarily involve abrasive wear and adhesive wear. The primary processing defects observed in the small holes include fiber pullout at the entrance and exit, as well as material delamination at the exit, which are primarily attributed to debonding at the layer interface and brittle fracture of the matrix. The subsequent drilling process, conducted based on laser drilling, effectively eliminates thermal defects such as the heat-affected zone and recast layer, as well as morphological defects like taper. Additionally, this approach mitigates the tool wear process, minimizes the impact of compression effects on material removal, and enhances the shear action of the tool. Consequently, the layer interface remains securely bonded and intact after processing, thereby preserving the material's strength.