Hasil untuk "Mechanical engineering and machinery"

Haimo Joehri

This paper gives an overview about aspects of mechanical engineering of undulators. It is based mainly on two types that are used in the SwissFEL facility. The U15 Undulator is an example of an in-vacuum type and the UE38 is an APPLE-X type. It describes the frame, the adjustment of the magnets with flexible keepers and the adjustment of the whole device with eccentric movers.

Patricia G. F. Matsubara, Tayana Conte

Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.

M. Erofeev, I. N. Kravchenko, M. Kryukov

Hwan-Jin Yoo, Go-Eun Kim, Chan-Jun Park et al.

Inverted organic light-emitting devices (OLEDs) have been attracting considerable attention due to their advantages such as high stability, low image sticking, and low operating stress in display applications. To address the charge imbalance that has been known as a critical issue of the inverted OLEDs, Li-doped MgZnO nanoparticles were synthesized as an electron-injection layer of the inverted OLEDs. Hexagonal wurtzite-structured Li-doped MgZnO nanoparticles were synthesized at room temperature via a solution precipitation method using LiCl, magnesium acetate tetrahydrate, zinc acetate dihydrate, and tetramethylammonium hydroxide pentahydrate. The Mg concentration was fixed at 10%, while the Li concentration was varied up to 15%. The average particle size decreased with Li doping, exhibiting the particle sizes of 3.6, 3.0, and 2.7 nm for the MgZnO, 10% and 15% Li-doped MgZnO nanoparticles, respectively. The band gap, conduction band minimum and valence band maximum energy levels, and the visible emission spectrum of the Li-doped MgZnO nanoparticles were investigated. The surface roughness and electrical conduction properties of the Li-doped MgZnO nanoparticle films were also analyzed. The inverted phosphorescent OLEDs with Li-doped MgZnO nanoparticles exhibited higher external quantum efficiency (EQE) due to better charge balance resulting from suppressed electron conduction, compared to the undoped MgZnO nanoparticle devices. The maximum EQE of 21.7% was achieved in the 15% Li-doped MgZnO nanoparticle devices.

Muneera Bano, Hashini Gunatilake, Rashina Hoda

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.

Hayato Ishida, Amal Elsokary, Maria Aslam et al.

Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.

Yue Guan

This paper explores the integration of a hands-on assignment in Mechanical Engineering studies, such as in the Mechanics of Machines course, with a focus on commonly used mechanisms like the four-bar and slider-crank mechanisms. Recognizing the challenges students face in connecting theoretical models to real-world applications, we implemented an innovative approach that utilizes affordable and easily accessible machinery toys. This approach consists of multiple carefully designed in-class and after-class tasks. Students actively assemble, identify, and quantitatively analyze the mechanisms in their assigned toys using the theoretical knowledge from the course lectures. The successful identification and analysis of kinematic pairs and mechanisms reflect a positive impact on students’ learning outcomes. Feedback from students indicated enhanced interest, comprehension, and confidence in their understanding of mechanical systems. The results demonstrate the effectiveness of this hands-on assignment in bridging the gap between theory and practice in mechanical engineering education, while also providing insights for future improvements in assignment design and timing. The proposed hands-on assignment can be easily replicated in any classroom setting, including remote learning environments.

Vladimir Tret'yakov

To enhance the competitiveness of domestic machinery mechanical engineering, to reduce excessive diversity in component parts and production components, as well as to address challenges related to digital transformation of production processes, new efficient business processes for product creation are required. Improving quality, technological sophistication, and operational efficiency of the parts depends on their design perfection. To solve design tasks, the author proposes three main tools namely coupling nodes, mobility matrices, and gaps between component parts of a product. Using these tools within the framework of group design methodology allows developing customized products with low costs per unit produced and will contribute to solving the task of digitizing business processes. Being the most general concepts that reflect what is inherent in any product, the proposed tools can help formalize the process of “inventing” a construction. The paper illustrates applying the proposed tools by an example of modernizing the design of a movable connection. In the original product, a moving part must be replaced with another type of structure. As a result of the project work, it is established that this requires introducing an additional component and designing its structure.

Ndiyamba David, Murena Eriyeti, Zendera Willard et al.

Enhancing practical skills training at universities requires the availability, adequacy, relevance, and proper maintenance of critical equipment and facilities. Improper maintenance of workshop facilities hampers effective teaching and the acquisition of skills. In this regard, this paper focuses on investigating the equipment needed for mechanical engineering institutional workshops, its failures, and its maintenance. Mixed methods were used, including a review of work on critical equipment for comprehensive engineering training based on educator and employer perspectives, with online research and physical visits employed to carry out observations. Microsoft Excel and Microsoft Access were used to analyze data and develop a computerized maintenance system to support the maintenance of training equipment and facilities. A priority list of essential facilities and machinery was developed, and maintenance plans were proposed based on a pilot study of two key machine tools, the milling machine and lathe, which were used in the experimental construction of an automated maintenance management system. This study can be utilized to enhance the skills and proficiencies of mechanical engineering graduates, enabling them to be employable and contribute positively to solving social and economic challenges.

Huan-Huan Zhao

Despite the rapid development of the AI industry and the programming industry in the machinery manufacturing or production industry, there is no systematic explanation as to why the use of machine learning can enhance the development of related industries. This paper summarizes the reasons for the industry’s development direction from the three aspects of machine learning’s automation design, expected maintenance, and production efficiency improvement. The learning types are briefly introduced to indicate the application of machine learning in mechanical engineering. By providing relevant examples, the superiority of machine learning in mechanical engineering is reflected, and specific practical cases are given to show the development trend of machine learning in the manufacturing industry in the future. Finally, we make predictions on the main development directions of machine learning in the future industry.

Marcus Kessel, Colin Atkinson

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.

Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito et al.

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.

Valerio Terragni, Annie Vella, Partha Roop et al.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.

Arthur Pilone, Paulo Meirelles, Fabio Kon et al.

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.

Kavekar Mukund, Thokal Gajanan, Tambuskar Dhanraj

Predictive maintenance (PdM) of machines using Artificial Intelligence (AI) and Machine Learning (ML) is an emerging and rapidly growing field within Mechanical Engineering. By leveraging AI and ML algorithms, engineers can analyse vast amounts of data collected from sensors and other monitoring devices to predict when machinery components will likely fail. In continuous flow production systems, the combination of AI and machine learning ML for PdM is presently reaching a peak., yet its penetration into machines within small and medium-scale enterprises remains comparatively subdued. The lathe machine is one of the important machine in small and medium-scale companies, PdM of lathe machines using AI & ML techniques could be the development of more accurate and robust models for early fault detection. While existing ML approaches failure prediction is based on historical data patterns, Regarding the precision and dependability of these forecasts, there might be space for improvement. In the present work, the loT based low cost data acquisition system is applied for condition monitoring, and data obtained is analyzed using AI & ML algorithms. This predictive approach enables maintenance to be performed before breakdowns occur, reducing downtime, minimizing costs, and improving overall efficiency and reliability of mechanical systems. This intersection of AI, ML, and mechanical engineering holds significant promise for optimizing maintenance practices and enhancing the performance of industrial machinery across various sectors.

Andrej Miklošík, Alexander Bernhard Krah

Companies from the mechanical engineering industry are eager to embrace new technologies in their pursuit of a competitive advantage. However, the complete digitalization of the sector encounters limitations, as certain aspects necessitate human supervision or manual labor. This is where the concepts of Industry 4.0, Industry 5.0, and digital transformation become relevant. The aim of the research presented in this paper was to gather and extract valuable insights and lessons from the experiences of German companies in the plastic extrusion machinery sector with digital transformation (DT). Qualitative interpretative research was used, using in-depth expert interviews with C-level executives. We organized the findings into three categories: (i) DT communication initiatives, including the elimination of paper, CRM solutions, messenger services, home office, and online procurement platforms; (ii) departments and areas most involved, including accounting and procurement, sales and production, and construction; and (iii) cost–benefit perception, including positive assessment, long-term impacts, and variation from company to company. The results provide valuable insights into the progress of DT initiatives in companies operating in the pipe extrusion sector in Germany. Additionally, several DT misconceptions were identified, thereby enriching the DT misconceptions framework that has been intensely discussed in the DT literature.

Patrick Kipkurui Ronoh, Charles M Mburu

The metal fabrication sector involves a variety of processes, activities, products, and by-products. This involves various interventions such  as milling, turning, welding, drilling, and grinding. Firms in this sector use one or a combination of these interventions where machinery  is used, which can expose workers to machinery hazards if proper safety procedures are not observed. Occupational Safety and Health  (OSH) is vital in Technical Vocational Education and Training (TVET) institutions, especially in mechanical engineering programmes where  metal fabrication is practised. The objective of this study was to assess the awareness of occupational machinery hazards in mechanical  engineering workshops in TVET Institutions. The study adopted a descriptive research design and employed a structured questionnaire  for data collection. Purposive sampling was used to identify institutions participating in the study. SPSS version 25 was used to analyse  the data and present it in tables and graphs. Noise (90.4%) and vibration (71.9%) were reported as the most common occupational  hazards, respectively, in mechanical engineering workshops. Regarding workstations, the grinding section (39.4%) and milling section  (15.8%) were reported as experiencing high levels of noise. The study recommends that adequate control measures be put in place to  mitigate against these hazards.

Jingyi Bai

In the development of modern mechanical engineering construction, the automation level and economic performance of mechanical engineering directly affect the effect of construction technology, so how to use electromechanical integration system, improve the construction quality of mechanical engineering field, has become the main problem discussed by researchers. Especially after entering the era of big data, mechatronics, as an interdisciplinary comprehensive research topic, integrates mechanical technology, automatic control, information technology and other contents, providing a new idea for technological innovation in the field of mechanical engineering. In this paper, on the basis of understanding the research status of mechatronics system, according to the basic structure of modern construction machinery system, in-depth discussion on how to apply mechatronics system in mechanical engineering, clear the future development direction of mechanical engineering construction.

Jian Yang, Lei Zhang, Jun Zhang et al.

In this study, the mechanical models of a multilayer combined extrusion cylinder and a steel-wire-winding extrusion cylinder were established and compared using a finite element simulation and existing experimental cases. This work provides theoretical support for the selection of an ultrahigh-pressure extrusion cylinder. Comparative analysis of an ultrahigh-pressure extrusion structure was carried out. The mathematical optimization model is established based on the mechanical model, and the ultimate bearing capacities of the schemes are compared. Additionally, the winding mode and the number of core layers of the extrusion cylinder are compared and analyzed, which provides a theoretical basis for the parameter design of the steel-wire-winding ultrahigh-pressure extrusion cylinder. This work holds good theoretical significance and practical value for the promotion and application of ultrahigh-pressure hydrostatic extrusion technology.

Tong Liu, Xueping Gu, Shaoyan Li et al.

Abstract The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy. Thus, this paper proposes a static voltage stability margin prediction method based on a graph attention network (GAT) and a long short‐term memory network (LSTM) to predict the static voltage stability margin of a power system accurately, fast, and effectively, considering new energy uncertainty. First, an innovative machine learning framework named the GAT‐LSTM is designed to extract highly representative power grid operation features considering the spatial‐temporal correlation of the power grid operation. Then, a static voltage stability margin prediction method based on the GAT‐LSTM is developed. Particularly, considering the influence of new energy power uncertainty, two loss functions of certainty and uncertainty are used in the proposed method to predict the voltage stability margin and voltage fluctuation range. Finally, the IEEE39‐bus power system and a practical power system are employed to verify the proposed method. The results show that the computational speed of the proposed method is greatly improved compared to the traditional methods not based on machine learning; the computation results are more accurate and reliable than the existing machine learning methods. Compared with the existing methods, the proposed method has higher scalability and applicability.