Hasil untuk "Engineering machinery, tools, and implements"

Ahmed Alhussen, N. Vinoth, M. Jenis et al.

Yupeng Qi, Ran Xu, Xu Chu

Large language models (LLMs) are establishing new paradigms for engineering applications by enabling natural language control of complex computational workflows. This paper introduces FeaGPT, the first framework to achieve complete geometry-mesh-simulation workflows through conversational interfaces. Unlike existing tools that automate individual FEA components, FeaGPT implements a fully integrated Geometry-Mesh-Simulation-Analysis (GMSA) pipeline that transforms engineering specifications into validated computational results without manual intervention. The system interprets engineering intent, automatically generates physics-aware adaptive meshes, configures complete FEA simulations with proper boundary condition inference, and performs multi-objective analysis through closed-loop iteration. Experimental validation confirms complete end-to-end automation capability. Industrial turbocharger cases (7-blade compressor and 12-blade turbine at \SI{110000}{rpm}) demonstrate the system successfully transforms natural language specifications into validated CalculiX simulations, producing physically realistic results for rotating machinery analysis. Additional validation through 432 NACA airfoil configurations confirms scalability for parametric design exploration. These results demonstrate that natural language interfaces can effectively democratize access to advanced computational engineering tools while preserving analytical rigor.

Johan Cederbladh, Eduard Kamburjan, David A. Manrique‐Negrin et al.

At its very core, model‐based systems engineering uses models to enable a multidisciplinary view on a system design in the early stages. These early stage models evolve horizontally: new diagrams for further perspectives and disciplines are added, using the same notation and the same abstraction level. Just as any other process in systems engineering, horizontal model evolution is subject to guidelines and standards, and the multidisciplinary view on a horizontal evolution, involving at least two disciplines, requires referring to multiple guidelines. Despite the significant effort invested in this process, there is no framework or tool support for engineering reviews of horizontal model evolution. In this paper, we aim to support engineering reviews by providing traceability for engineering activities that evolve a model horizontally. Our contribution is a process‐agnostic framework that relies on capturing the intent of model changes in addition to the changes themselves. We group the model changes into transactional units called deltas, which are subsequently annotated with the engineer's intent to perform these specific changes. We give a methodology to integrate such intent‐annotated deltas into engineering reviews and audits, an ontology to capture intent, and a meta‐model for the deltas to achieve a language‐ and guideline‐independent framework. We use an example from the earth moving machinery domain to exemplify the need for horizontal model evolution and provide a prototypical proof‐of‐concept implementation in the SysML Papyrus Plugin for Eclipse and a SysML case study using a machine brake system.

V. Teterin, N. S. Panferov, A. Khortov et al.

Mechanization of potato harvesting is a long process of evolution that began with the introduction of the simplest tools. These devices were gradually improved, which led to their replacement with more complex machines, which made it possible to significantly increase labor productivity and process large areas of land in a short time. (Research purpose) The research purpose is identifying the main stages and patterns of development of potato harvesters, as well as analyzing the factors influencing this process. (Materials and methods) The development of potato harvesting equipment was studied using various methodological directions, such as externalism, internalism and the historical and analytical method. The influence of external factors on the development of agricultural machinery is multidimensional and depends on a variety of circumstances that may change over time. Scientific research, engineering developments and technological innovations play a key role in the creation of new models of potato harvesters, ensuring their efficiency and productivity. (Results and discussion) Presented the history of mechanization of potato harvesting, starting with the first attempts at the beginning of the XX century. and ending with the development of more modern potato harvesters in the post-war years. The main attention was paid to the evolution of structures, increasing efficiency and reducing labor costs during harvesting. (Conclusions) The process of mechanization of potato harvesting is a complex evolutionary trajectory that began with the introduction of elementary agricultural implements. Over time, agrotechnical tools underwent modernization and optimization, which led to their gradual replacement with more complex and technologically advanced machines. This transformation reflects significant progress in the field of agricultural engineering and mechanization of agricultural production, contributing to increased efficiency and productivity in the field of potato growing.

Khadija Talbi, Zineb Ait Haddouchane, Soumia Bakkali et al.

The rapid advancement of digital technologies, referred to as Industry 4.0, has profoundly transformed the manufacturing landscape, necessitating a reevaluation of engineering education. Future engineers must possess diverse skills and competencies to effectively navigate this new era of intelligent, interconnected, and data-driven production systems. In response to this challenge, this research paper introduces a framework for a virtual laboratory in mechanical and industrial engineering that creates a laboratory in virtual reality (VR) by integrating Lean Manufacturing principles to optimize flow shop processes, thereby preparing engineering students for the demands of Industry 4.0. This approach prepares students to navigate the challenges of modern manufacturing, bridging the gap between theoretical knowledge and its practical application. This paper will discuss the concept of the virtual laboratory for mechanical and industrial engineering education in the Moroccan context based on lean principles.

Shogo NAKAMURA, Kenichi NAKANISHI, Kenji OHARA et al.

Low-frequency vibration cutting (VC) is a machining technology wherein a tool periodically vibrates to break cutting chips. However, VC produces tool paths that include air cuts to break up chips, which deteriorates surface roughness and roundness. This study aims to address these limitations. Low-frequency vibrations are applied to both the upper and lower turrets of a multitasking lathe. To improve surface roughness with simultaneous VC, this study presents a theoretical method to calculate the optimal values for frequency and amplitude of VC and the starting positions of the Z-axis of two upper and lower turrets. Simulations and actual machining experiments were conducted after adjusting VC frequency and amplitude and the starting positions of Z-axis of both turrets. Results show that the surface roughness improves with two-turret simultaneous VC compared to one-turret single VC in stainless steel and brass machining experiments, while succeeding in breaking up cutting chips. Furthermore, the surface roughness of simultaneous VC is improved to a level close to that of single and simultaneous conventional cutting (CC) results in stainless steel. Although there is some variation in material, the roundness of simultaneous VC is generally improved compared to single VC and CC. For the machining of brass, the roundness results of the simultaneous VC were better than those of single CC, VC, and simultaneous CC.

Roham Koohestani, Philippe de Bekker, Begüm Koç et al.

Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.

Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan

The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems as part of the emerging Software Engineering 3.0 era. Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution. This process involves dynamic optimization of cognitive architectures and their constituents (e.g., prompts, foundation model configurations, and system parameters) while finding the optimal trade-off between several objectives, such as accuracy, cost, and latency. This paper outlines the architecture of Compiler.next and positions it as a cornerstone in democratizing software development by lowering the technical barrier for non-experts, enabling scalable, adaptable, and reliable AI-powered software. We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search heuristics, reproducibility, and interoperability between compilers. Our vision lays the groundwork for fully automated, search-driven software development, fostering faster innovation and more efficient AI-driven systems.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

Kevin Hermann, Sven Peldszus, Jan-Philipp Steghöfer et al.

Software security is of utmost importance for most software systems. Developers must systematically select, plan, design, implement, and especially, maintain and evolve security features -- functionalities to mitigate attacks or protect personal data such as cryptography or access control -- to ensure the security of their software. Although security features are usually available in libraries, integrating security features requires writing and maintaining additional security-critical code. While there have been studies on the use of such libraries, surprisingly little is known about how developers engineer security features, how they select what security features to implement and which ones may require custom implementation, and the implications for maintenance. As a result, we currently rely on assumptions that are largely based on common sense or individual examples. However, to provide them with effective solutions, researchers need hard empirical data to understand what practitioners need and how they view security -- data that we currently lack. To fill this gap, we contribute an exploratory study with 26 knowledgeable industrial participants. We study how security features of software systems are selected and engineered in practice, what their code-level characteristics are, and what challenges practitioners face. Based on the empirical data gathered, we provide insights into engineering practices and validate four common assumptions.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Timotius Anggit Kristiawan, T. Setiyawan, Farika Tono Putri et al.

In the current technological era, plastic assumes a crucial role in daily life. This is substantiated by the continuously escalating demand for plastic-based products. Manufacturing items by reshaping plastic raw materials necessitates the use of plastic forming tools or machinery. A prevalently utilized apparatus is the injection molding machine, which employs a specialized mold. The mold design process mandates rigorous calculations and planning to ensure the production of dimensionally identical and uniform parts, thereby facilitating mass production. The primary objective of this research was to design, implement, and validate a mold for plastic injection molding, specifically intended for the mass production of keychains. The methodology employed was the Sighley's Mechanical Engineering Design approach, which encompasses the following sequential procedures: Problem Identification, Problem Definition, Synthesis, Analysis and Optimization, Evaluation, and Presentation. The fabricated keychain mold yielded two products per injection cycle, with mold dimensions (L×W×H) measuring 110 mm×100 mm×160 mm. The mold was mounted on the injection molding machine, requiring a clamping force of 46710 N, with system control provided by an Arduino and cooling facilitated by an external fan. The test results identified the optimal operating parameters for a single molding process, comprising a heating temperature of 210°C, an injection time of 17 seconds, and a cooling time of 12 seconds.

R. Maksimov

BACKGROUND: the modern methods of scientific research in the field of vehicle dynamics are reduced to the creation of complex complete vehicles motion models and the further realization of simulation procedures using numerical methods. With the help of this approach, it becomes possible to solve problems of determining the internal parameters of vehicle components and assemblies systems, assessing the system functioning quality under study as a whole, or its individual components, while taking into account the complex functioning of all its internal subsystems. AIMS: tools development in the form of highway road train motion model in Multy-Body Dynamics environment for predicting the vehicle operational properties, analyzing design and engineering solutions, and selecting vehicle systems parameters. MATERIALS AND METHODS: the study uses numerical simulation methods in Multy-Body Dynamics environment Simcenter AMESim. The simulation object is a vehicle highway truck unit type, moving during operation along 1st and 2nd irregularities categories roads, both in a single state and with a semi-trailer as part of a highway road train. The functioning adequacy of the developed motion model has been confirmed by experimental validation methods based on the results of real highway road train field road tests at the polygon. RESULTS: a complex spatial motion model of the highway road train has been developed to simulate its movement in various weight states along the 1st and 2nd roads irregularities categories in Simcenter AMESim. The motion model provides opportunities for predicting the operational properties of smooth running, stability, controllability, traction and braking dynamics, for analyzing design and engineering solutions, and for selecting vehicle system parameters. The results of the developed motion model validation are shown deviations of the simulation results within 10%. CONCLUSIONS: a complex spatial motion model of the highway road train and its software implementation for modeling its dynamics in order to predict the vehicle operational properties have been developed. The developed model is applicable for researches of vehicles dynamics indicators by simulation modeling methods with allowable simulation results deviations within 10%.

Riana Magdalena Silitonga, Ferdian Aditya Pratama, Ronald Sukwadi

Due to the COVID-19 pandemic, most schools and colleges have adopted hybrid learning. Hybrid learning, also called “blended learning,” mixes online and classroom instruction. Blended learning may become permanent as face-to-face and internet-based education become more accepted. We examined how hybrid learning affects the understanding of Indonesian students majoring in Industrial Engineering at Atma Jaya Catholic University. In this study, understanding matched learning efficacy. An experimental design was used to measure component influence. In strategic planning, strengths, weaknesses, opportunities, and threats (SWOT) analysis was used as an effective tool to examine an organization’s internal and external variables with a learning methodology design. A questionnaire survey was conducted to measure the understanding of the SWOT analysis results and the related strategy. A total of 96 participants were involved in this study. The mixed learning method, using the weakness–opportunity or mini–maxi strategy with the divestment–investment principle, was found to be effective.

Karina Niyazova, Assel Mukasheva, Gani Balbayev et al.

The fight against financial crimes has become increasingly challenging, and the need for sophisticated systems that can accurately identify suspicious transactions has become more pressing. The goal of the study is to develop a new feature selection method based on swarm intelligence algorithms to improve the quality of data classification. This article is about the development of an information system for the classification of transactions into legal and suspicious in an anti-money laundering sphere. The system utilizes a swarm-algorithm-based feature selection approach, specifically the ant colony optimization algorithm, which was both used and adapted for this purpose The article also presents the system’s functional–structural diagram and feature selection algorithm flowchart. The proposed feature selection method can be used to classify data from various subject areas.

Stanislav Eroshenko, Evgeniy Shmakov, Dmitry Klimenko et al.

This paper explores the application of conceptual hydrological models in optimizing the operation of hydroelectric power plants (HPPs) in steppe regions, a crucial aspect of promoting low-carbon energy solutions. The study aims to identify the most suitable conceptual hydrological model for predicting reservoir inflows from multiple catchments in a steppe region, where spring runoff dominates the annual water volume and requires careful consideration of snowfall. Two well-known conceptual models, HBV and GR6J-CemaNeige, which incorporate snow-melting processes, were evaluated. The research also investigated the best approach to preprocessing historical data to enhance model accuracy. Furthermore, the study emphasizes the importance of accurately defining low-water periods to ensure reliable HPP operation through more accurate inflow forecasting. A hypothesis was proposed to explore the relationship between atmospheric circulation and the definition of low-water periods; however, the findings did not support this hypothesis. Overall, the results suggest that combining the conceptual models under consideration can lead to more accurate forecasts, underscoring the need for integrated approaches in managing HPP reservoirs and promoting sustainable energy production.

Paulo Monteiro de Carvalho Monson, Vinicius Augusto Dare de Almeida, Gabriel Augusto David et al.

Computer vision applications demand a significant amount of data for effective training and inference in many computer vision tasks. However, data insufficiency situations usually happen due to multiple reasons, resulting in computational models whose performance is inadequate. Traditional data augmentation techniques are presented to solve this overfitting problem; however, their application is not always possible or desirable. In this context, this paper addresses a different data augmentation technique for classification methods based on adversarial images to reduce the impact of sample imbalance utilizing the Fast Gradient Sign Method (FGSM) with added noise to enhance classifier performance. To validate the method, a set of images was used for the classification of diseases in coffee plants due to the soil’s lack of nutrients. The results showed an improvement in the model performance for this classification in coffee plants proving the validity of the proposed method, which can be used as an alternative to traditional data augmentation methods.

Ranim Khojah, Mazen Mohamad, Philipp Leitner et al.

Large Language Models (LLMs) are frequently discussed in academia and the general public as support tools for virtually any use case that relies on the production of text, including software engineering. Currently there is much debate, but little empirical evidence, regarding the practical usefulness of LLM-based tools such as ChatGPT for engineers in industry. We conduct an observational study of 24 professional software engineers who have been using ChatGPT over a period of one week in their jobs, and qualitatively analyse their dialogues with the chatbot as well as their overall experience (as captured by an exit survey). We find that, rather than expecting ChatGPT to generate ready-to-use software artifacts (e.g., code), practitioners more often use ChatGPT to receive guidance on how to solve their tasks or learn about a topic in more abstract terms. We also propose a theoretical framework for how (i) purpose of the interaction, (ii) internal factors (e.g., the user's personality), and (iii) external factors (e.g., company policy) together shape the experience (in terms of perceived usefulness and trust). We envision that our framework can be used by future research to further the academic discussion on LLM usage by software engineering practitioners, and to serve as a reference point for the design of future empirical LLM research in this domain.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.

Jukka Ruohonen, Kalle Hjerppe

Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.