Hasil untuk "Mechanical engineering and machinery"

C. Harris, C. Crede, H. Trent

N. Aimeur, A. Amour, N. Menasri

Rotating machinery such as centrifugal fans is often exposed to unsteady aerodynamic loads that can induce fatigue damage during prolonged industrial operation. This study investigates the fatigue life of a centrifugal fan (FN280) used in a cement plant through a one-way fluid–structure interaction (FSI) approach. Transient aerodynamic loads were computed using ANSYS Fluent and subsequently transferred to ANSYS Mechanical for structural fatigue analysis. Since the original fan geometry was unavailable, a detailed three-dimensional model was reconstructed via reverse engineering to ensure accurate aerodynamic representation. The unsteady numerical simulations were performed under four operating conditions and validated against experimental performance data. The comparison shows a satisfactory level of agreement, confirming the reliability of the adopted modelling approach. Simulation results indicate that most regions of the fan exhibit fatigue safety factors between 1 and 15, suggesting generally safe operation. However, localized stress concentrations near the blade root and shaft–disc junction display safety factors slightly below unity, indicating potential sites for early fatigue crack initiation. The estimated fatigue life is approximately 2.8 × 10⁶ cycles, emphasizing the significance of accounting for aerodynamic loading effects in fatigue assessment. Overall, the study demonstrates the capability of FSI-based numerical simulations for predictive maintenance and reliability evaluation, while further experimental validation is recommended.

Murat Şen, Mesut Hüseyinoglu, Mehmet Erbil Özcan et al.

This study investigated the dynamic behavior of AA7075 plates joined by Friction Stir Welding (FSW), focusing on the influence of key process parameters, rotation, and traverse speeds, on the resulting dynamic characteristics. Experimental Modal Analysis (EMA) was performed under free boundary conditions to determine resonance frequencies, mode shapes, and damping ratios, revealing that an increase in traverse speed consistently led to a decrease in natural frequencies across most modes, thereby indicating reduced joint stiffness attributed to insufficient heat input. Furthermore, localized weld defects caused significant damping variations, particularly in low-order modes. To complement the experimental findings and enable simultaneous, multi-output prediction of these coupled dynamic parameters, a Co-Active Neuro-Fuzzy Inference System (CANFIS) model was developed. The CANFIS architecture utilized spindle speed and feed rate as inputs to predict natural frequency and damping ratio for multiple vibration modes as tightly coupled outputs. The trained model demonstrated strong agreement and high predictive accuracy against the EMA experimental data, with convergence analysis confirming its stable learning and excellent generalization capability. The successful integration of EMA and CANFIS establishes a robust hybrid framework for both physical interpretation and intelligent, coupled prediction of the dynamic behavior of FSW-welded AA7075 plates.

Kadir Kozan, Ozgur Keles, Sihan Jian et al.

GenAI has a potential to enhance the learning and teaching processes in engineering education. For instance, GenAI feedback on students' task performance can be effective depending on when such feedback is provided. However, little is known about how engineering faculty and instructors discover such potential within the scope of their instruction when they try out the technology for the first time. To this end, this study purported to describe an engineering instructor's and seven teaching assistants' initial experiences of integrating GenAI into their undergraduate engineering course and the corresponding changes in students' formative exercise performance. An embedded descriptive single case study design was employed. The corresponding research data included four interviews conducted at the beginning, middle and end of an academic semester, and students' formative exercise performance. Overall, after GenAI integration, students' formative exercise performance increased, and a critical and reflective practice of learning about how to integrate GenAI into instruction provided informative insights. Still, technology integration stayed at the level of replacing other instructional methods or increasing the efficiency of solving coding problems. It turned out to be exciting and surprising for students to be able to use GenAI in course work even though their use of the technology weakened over time. Our findings suggest that engineering teaching staff's initial experimental experiences with GenAI integration can be informative and provide context-specific practical insights. Therefore, it is reasonable for higher education institutions to encourage such experiences especially when there is a lot of unknown regarding an emerging technology.

Weihao Zhang, Yitong Zhou, Huanyu Qu et al.

As LLM-based multi-agent systems (MAS) become more autonomous, their free-form interactions increasingly dominate system behavior. However, scaling the number of agents often amplifies context pressure, coordination errors, and system drift. It is well known that building robust MAS requires more than prompt tuning or increased model intelligence. It necessitates engineering discipline focused on architecture to manage complexity under uncertainty. We characterize agentic software by a core property: \emph{runtime generation and evolution under uncertainty}. Drawing upon and extending software engineering experience, especially object-oriented programming, this paper introduces \emph{Loosely-Structured Software (LSS)}, a new class of software systems that shifts the engineering focus from constructing deterministic logic to managing the runtime entropy generated by View-constructed programming, semantic-driven self-organization, and endogenous evolution. To make this entropy governable, we introduce design principles under a three-layer engineering framework: \emph{View/Context Engineering} to manage the execution environment and maintain task-relevant Views, \emph{Structure Engineering} to organize dynamic binding over artifacts and agents, and \emph{Evolution Engineering} to govern the lifecycle of self-rewriting artifacts. Building on this framework, we develop LSS design patterns as semantic control blocks that stabilize fluid, inference-mediated interactions while preserving agent adaptability. Together, these abstractions improve the \emph{designability}, \emph{scalability}, and \emph{evolvability} of agentic infrastructure. We provide basic experimental validation of key mechanisms, demonstrating the effectiveness of LSS.

Madalena Sasportes, Grischa Liebel, Miguel Goulão

Context: Autism spectrum disorder (ASD) leads to various issues in the everyday life of autistic individuals, often resulting in unemployment and mental health problems. To improve the inclusion of autistic adults, existing studies have highlighted the strengths these individuals possess in comparison to non-autistic individuals, e.g., high attention to detail or excellent logical reasoning skills. If fostered, these strengths could be valuable in software engineering activities, such for identifying specific kinds of bugs in code. However, existing work in SE has primarily studied the challenges of autistic individuals and possible accommodations, with little attention their strengths. Objective: Our goal is to analyse the experiences of autistic individuals in software engineering activities, such as code reviews, with a particular emphasis on strengths. Methods: This study combines Social-Technical Grounded Theory through semi-structured interviews with 16 autistic software engineers and a survey with 49 respondents, including 5 autistic participants. We compare the emerging themes with the theory by Gama et al. on the Effect of Neurodivergent Cognitive Dysfunctions in Software Engineering Performance. Results: Our results suggest that autistic software engineers are often skilled in logical thinking, attention to detail, and hyperfocus in programming; and they enjoy learning new programming languages and programming-related technologies. Confirming previous work, they tend to prefer written communication and remote work. Finally, we report a high comfort level in interacting with AI-based systems. Conclusions: Our findings extend existing work by providing further evidence on the strengths of autistic software engineers.

Chiara Pasini, Oscar Ramponi, Stefano Pandini et al.

Lattice structures have great potential for several application fields ranging from medical and tissue engineering to aeronautical one. Their development is further speeded up by the continuing advances in additive manufacturing technologies that allow to overcome issues typical of standard processes and to propose tailored designs. However, the design of lattice structures is still challenging since their properties are considerably affected by numerous factors. The present paper aims to propose, discuss, and compare various modeling approaches to describe, understand, and predict the correlations between the mechanical properties and the void volume fraction of different types of lattice structures fabricated by fused deposition modeling 3D printing. Particularly, four approaches are proposed: (i) a simplified analytical model; (ii) a semi-empirical model combining analytical equations with experimental correction factors; (iii) an artificial neural network trained on experimental data; (iv) numerical simulations by finite element analyses. The comparison among the various approaches, and with experimental data, allows to identify the performances, advantages, and disadvantages of each approach, thus giving important guidelines for choosing the right design methodology based on the needs and available data.

Suraj Kumar, Aditya Rallapalli, Bharat Kumar GVP

Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the propulsion dynamics is essential for analyzing closed-loop guidance and control schemes during descent. This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics using data obtained from high-fidelity propulsion model. The developed model is validated with experimental results and used for closed-loop guidance and control simulations.

Emily Vorderwülbeke, Isabella Graßl

Queer students often encounter discrimination and a lack of belonging in their academic environments. This may be especially true in heteronormative male-dominated fields like software engineering, which already faces a diversity crisis. In contrast, disciplines like humanities have a higher proportion of queer students, suggesting a more diverse academic culture. While prior research has explored queer students' challenges in STEM fields, limited attention has been given to how experiences differ between the sociotechnical, yet highly heteronormative, field of software engineering and the socioculturally inclusive humanities. This study addresses that gap by comparing 165 queer software engineering and 119 queer humanities students experiences. Our findings reveal that queer students in software engineering are less likely to be open about their sexuality, report a significantly lower sense of belonging, and encounter more academic challenges compared to their peers in the humanities. Despite these challenges, queer software engineering students show greater determination to continue their studies. These insights suggest that software engineering could enhance inclusivity by adopting practices commonly seen in the humanities, such as integrating inclusive policies in classrooms, to create a more welcoming environment where queer students can thrive.

R. Isermann

Qi Wang, Hongye Zhang, L. Hao et al.

Superconducting (SC) magnets can generate exceptionally high magnetic fields and can be employed in various applications to enhance system power density. In contrast to conventional coil-based SC magnets, high-temperature superconducting (HTS) trapped field magnets (TFMs), namely HTS trapped field bulks (TFBs) and trapped field stacks (TFSs), can eliminate the need for continuous power supply or current leads during operation and thus can function as super permanent magnets. TFMs can potentially trap very high magnetic fields, with the highest recorded trapped field reaching 17.89 T, achieved by TFSs. TFMs find application across diverse fields, including rotating machinery, magnetic bearings, energy storage flywheels, and magnetic resonance imaging. However, a systematic review of the advancement of TFMs over the last decade remains lacking, which is urgently needed by industry, especially in response to the global net zero target. This paper provides a comprehensive overview of various aspects of TFMs, including simulation methods, experimental studies, fabrication techniques, magnetisation processes, applications, and demagnetisation issues. Several respects have been elucidated in detail to enhance the understanding of TFMs, encompassing the formation of TFBs and TFSs, trapped field patterns, enhancement of trapped field strength through pulsed field magnetisation, as well as their applications such as SC rotating machines, levitation, and Halbach arrays. Challenges such as demagnetisation, mechanical failure, and thermal instability have been illuminated, along with proposed mitigation measures. The different roles of ferromagnetic materials in improving the trapped field during magnetisation and in reducing demagnetisation have also been summarised. It is believed that this review article can provide a useful reference for the theoretical analysis, manufacturing, and applications of TFMs within various domains such as materials science, power engineering, and clean energy conversion.

Kai Chai, Yong Liu, B. Hu

When vibrations generated by marine machinery propagate through a ship’s hull into the ocean, they produce low-frequency radiated noise with distinct “acoustic fingerprint” characteristics. This noise, characterized by stable and concentrated energy, long transmission distances, and difficulty in elimination, becomes the primary target for enemy sonar detection. Active vibration isolation serves as a critical method for reducing low-frequency vibrations in ships and enhancing their acoustic stealth performance. However, control challenges persist, including multi-frequency excitation, frequency fluctuation, multi-channel coupling, and slow convergence speed. To address these issues, this paper introduced an innovative multi-channel decentralized decoupling filtered-x least mean square (DMFxLMS) algorithm. Firstly, a recursive least squares identification algorithm with a forgetting factor was proposed, taking into account the characteristics of single-input, multi-output and multi-input, and multi-output control systems, effectively enhancing the algorithm’s convergence speed and control accuracy. Secondly, based on the decentralized decoupling control concept, the multi-channel control system was simplified into parallel single-channel control loops. The control weight coefficient updates were only related to adjacent error signals, significantly reducing the algorithm’s computational complexity. Thirdly, an anti-impact link was designed to improve the algorithm’s robustness, considering the interference caused by other mechanical equipment during the control process. The influence of abnormal error signals in the control weight coefficient correction term was suppressed, and a percentage function was introduced to limit the output signal. Finally, the feasibility and effectiveness of the DMFxLMS algorithm were verified through simulations and experiments. The results demonstrated that the DMFxLMS algorithm achieved significant control effects for both constant frequency line spectrum excitation and frequency fluctuating line spectrum excitation, fulfilling the objective of reducing base vibration. The DMFxLMS algorithm exhibited fast convergence and excellent robustness, making it suitable for practical engineering applications.

Oksana Ayurova, V. Kornopoltsev, A. Khagleev et al.

Advancements in industrial machinery and manufacturing equipment require more reliable and efficient polymer tribo-systems which operate in conditions associated with increasing machine speeds and a lack of cooling oil. The goal of the current research is to improve the tribological properties of elastomeric composites by adding a solid lubricant filler in the form of ultrafine polytetrafluoroethylene (PTFE) with the chemical formula [C2F4]n and recycled polytetrafluoroethylene (r-PTFE) powders. PTFE waste is recycled mechanically by abrasion. The elastomeric composites are prepared by mixing a nitrile butadiene rubber with a phenol-formaldehyde resin and PTFE powders in an extruder followed by rolling. The deformation-strength and tribological tests of r-PTFE elastomeric composites are conducted in comparison with the ultrafine PTFE composites. The latter is based on products of waste fluoropolymer processing using a radiation method. The deformation-strength test shows that the introduction of ultrafine PTFE and r-PTFE powder to the composite leads to a decrease in strength and elongation at break, which is associated with the poor compatibility of additives and the elastomeric matrix. The friction test indicates a decrease in the coefficient of friction of the composite material. It is determined that the 15 wt.% filler added in the elastomeric matrix leads to a reduction in the wear rate by 20%. The results obtained show the possibility of using ultrafine PTFE powder and r-PTFE for creating elastomeric composites with increased tribological properties. These research results are beneficial for rubber products used in many industries, mainly in mechanical engineering.

Wang Yueqin, Tan Xiaolan, Ban Xiang et al.

To meet the requirement of mobile robots to achieve high mobility and strong obstacle-crossing in complex and variable environment, a design scheme of passive transformable wheel-legged obstacle-crossing robot is proposed. The transformable wheel conversion process of the robot is obtained by external force operation, so no additional driver is required, which reduces the complexity of the mechanism. Firstly, based on the three-dimensional modeling of the whole robot, the structure, principle and force of the transformable wheel are analyzed, and then the structure optimization is carried out by taking the ratio of the triggering torque during the transformation process and radius before and after unfolding as the index. Afterwards, the force condition during the transformable stage of the robot is analyzed and the relevant parameters of the robot platform are adjusted to achieve stable obstacle crossing. Finally, kinematics simulation of transformation and obstacle-crossing process of the robot is carried out by using the Adams software, physical prototype is made and rationality of the structure design of the whole machine is verified by experiment.

G. Arun, C. N. Marimuthu

Big data analytics is a complex exploratory process to uncover hidden data information from vast collections of data. It often provides enormous information from diverse sources and the use of analytics provides confined knowledge from the collected noisy data. In the case of diabetes data, there exist a massive collection of patient data that relates to significant information on patient health and its critical nature. In order of validating and analysing the data to get desired information about a patient and their health risk from the vast collection of data, the study uses bigdata based deep learning analytics. This study uses a Deep Learning Model namely capsule network (CapsNet) is executed on a MapReduce framework. The CapsNet present in the MapReduce framework enables the classification of instances via proper regulations. This model after suitable training with the training dataset enables optimal classification of instances to detect the nature of the risk of a patient. The validation conducted on the test dataset shows that the proposed CapsNets-based MapReduce model obtains increased accuracy, recall, and F-score than the conventional MapReduce and deep learning models.

Shangpeng Zhong, Xiaoming Wang, Bin Xu et al.

This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic (PV) power prediction that arises due to insufficient data samples for new PV plants. First, a time-series generative adversarial network (TimeGAN) is used to learn the distribution law of the original PV data samples and the temporal correlations between their features, and these are then used to generate new samples to enhance the training set. Subsequently, a hybrid network model that fuses bi-directional long-short term memory (BiLSTM) network with attention mechanism (AM) in the framework of deep & cross network (DCN) is constructed to effectively extract deep information from the original features while enhancing the impact of important information on the prediction results. Finally, the hyperparameters in the hybrid network model are optimized using the whale optimization algorithm (WOA), which prevents the network model from falling into a local optimum and gives the best prediction results. The simulation results show that after data enhancement by TimeGAN, the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

Description: Since its first appearance in 1950, Pounder's Marine Diesel Engines has served seagoing engineers, students of the Certificates of Competency examinations and the marine engineering industry throughout the world. Each new edition has noted the changes in engine design and the influence of new technology and economic needs on the marine diesel engine. Now in its ninth edition, Pounder's retains the directness of approach and attention to essential detail that characterized its predecessors. There are new chapters on monitoring control and HiMSEN engines as well as information on developments in electronic-controlled fuel injection. It is fully updated to cover new legislation including that on emissions and provides details on enhancing overall efficiency and cutting CO2 emissions. After experience as a seagoing engineer with the British India Steam Navigation Company, Doug Woodyard held editorial positions with the Institution of Mechanical Engineers and the Institute of Marine Engineers. He subsequently edited The Motor Ship journal for eight years before becoming a freelance editor specializing in shipping, shipbuilding and marine engineering. He is currently technical editor of Marine Propulsion and Auxiliary Machinery, a contributing editor to Speed at Sea, Shipping World and Shipbuilder and a technical press consultant to Rolls-Royce Commercial Marine. Helps engineers to understand the latest changes to marine diesel engineers Careful organisation of the new edition enables readers to access the information they require Brand new chapters focus on monitoring control systems and HiMSEN engines. Over 270 high quality, clearly labelled illustrations and figures to aid understanding and help engineers quickly identify what they need to know. Contents: Theory and general principles Exhaust emissions and control Fuels and lubes Performance Engine and Plant selection Pressure charging Fuel injection Low speed engines introduction MAN B&W low speed engines Mitsubishi low speed engines (Sulzer) low speed engines Burmeister & Wain, MAN and Doxford low speed engine chapters Four-stroke engines introduction Dual fuel and gas engines Allen medium speed engines Alpha Diesel medium speed engines Caterpillar HiMSEN engines Deutz MaK medium speed engines MAN medium speed engines Rolls-Royce Bergen medium speed engines SEMT-Pielstick engines Sulzer medium speed engines Wartsila medium speed engines Other medium speed engines Low speed four-stroke trunk piston engines High speed engines Gas turbines:

Qiwen Emma Lei, Jing Shu, Junming Wang et al.

Adolescent idiopathic scoliosis is a common condition that affects children between the age of 10 and young adulthood. Rigid brace treatment is an effective treatment to control the progression of spinal deformity. However, it limits mobility and causes discomfort, which leads to low treatment compliance. In this study, we developed and characterized a kirigami-inspired CT/MRI compatible spring that could be employed to modify our previously designed exoskeleton hinge vertebrae to provide immediate in-brace correction, good wear comfort, and one that does not inhibit mobility simultaneously. Additive manufacturing has drawn significant interest in academic and industrial terms due to its ability to produce geometrically complex structures. The structural design and dimension of the proposed 3D printed kirigami-inspired springs were optimized with the finite element method (FEM). The carbon-fiber-reinforced nylon material (PA-CF) was selected as the material of the kirigami-inspired spring with the balance of printing easiness and performance of the material. The stiffness of designed kirigami-inspired springs varied between 1.20 and 42.01 N/mm. A case series study with three scoliosis patients has been conducted to investigate the immediate in-brace effect on reducing the spinal curvature and asymmetry of the body contours using radiographic examination. The experiment results show that there are 4.6%–50.5% improvements in Cobb angle for different sections of spines. The X-ray images proved that our kirigami-inspired springs would not block views for Cobb angle measurements.

Vinicius Soares Silva Marques, Laurence Rodrigues do Amaral

Documentation is one of the most neglected activities in Software Engineering, although it is an important method of assuring quality and understanding. Bioinformatics software is generally written by researchers from fields other than Computer Science who usually do not provide documentation. Documenting bioinformatics software may ease its adoption in multidisciplinary teams and expand its impact on the community. In this paper, we highlight how one can document software that is already finished, using reverse engineering and thinking of the end-user.

Polra Victor Falade

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using the blog mining technique. Generative AI models have revolutionized the field of cyberattacks, empowering malicious actors to craft convincing and personalized phishing lures, manipulate public opinion through deepfakes, and exploit human cognitive biases. These models, ChatGPT, FraudGPT, and WormGPT, have augmented existing threats and ushered in new dimensions of risk. From phishing campaigns that mimic trusted organizations to deepfake technology impersonating authoritative figures, we explore how generative AI amplifies the arsenal of cybercriminals. Furthermore, we shed light on the vulnerabilities that AI-driven social engineering exploits, including psychological manipulation, targeted phishing, and the crisis of authenticity. To counter these threats, we outline a range of strategies, including traditional security measures, AI-powered security solutions, and collaborative approaches in cybersecurity. We emphasize the importance of staying vigilant, fostering awareness, and strengthening regulations in the battle against AI-enhanced social engineering attacks. In an environment characterized by the rapid evolution of AI models and a lack of training data, defending against generative AI threats requires constant adaptation and the collective efforts of individuals, organizations, and governments. This research seeks to provide a comprehensive understanding of the dynamic interplay between generative AI and social engineering attacks, equipping stakeholders with the knowledge to navigate this intricate cybersecurity landscape.