Hasil untuk "Engineering machinery, tools, and implements"

Duc Hoan T., Lai Dang G.

The stress–strain state in the cup wall plays a central role in axisymmetric deep drawing but is often simplified as uniaxial tension in analytical and numerical analyses. This study presents a numerical investigation of the cup-wall stress–strain state using axisymmetric finite element simulations in DEFORM-2D to assess the validity of this assumption. The effects of punch–die clearance, die corner radius, and blank-holder force are examined using a Box–Behnken design as a structured sampling of the process parameter space. Stress and strain components are extracted at three representative locations across the wall thickness, near the punch side, mid-thickness, and near the die side, at a consistent forming stage corresponding to the onset of stable wall formation. Principal stresses, principal strains, equivalent stress, and stress triaxiality are evaluated to characterize local mechanical states. The results reveal pronounced through-thickness heterogeneity of the stress–strain state. The punch-side region is dominated by compressive or mixed stress states, the mid-thickness exhibits a plane-stress-dominated biaxial tension that may approach but does not reach uniaxial tension under large clearance conditions, and the die-side region shows a stable tensile-dominated response. No strictly uniaxial tensile stress state is observed in the cup wall. These findings clarify the limitations of simplified stress assumptions and emphasize the need for three-dimensional stress analysis in deep drawing.

Andreas Rausch, Stefan Wittek, Tobias Geger et al.

Generative AI and agentic tools are reshaping agile software development, yet many engineering curricula still teach agile methods and AI competencies separately and largely lecture-based. This paper presents a project-based AI Engineering curriculum designed to prepare students for AI-driven agile development by integrating agile practices and AI-enabled engineering throughout the program. We contribute (1) the curriculum concept and guiding principles, (2) a case study of interdisciplinary, AI-enabled agile student projects, and (3) early evidence from a mixed-methods evaluation. In our case study, second-semester bachelor students work in teams over seven two-week sprints on a realistic software product. AI tools are embedded into everyday agile engineering tasks - requirements clarification, backlog refinement, architectural reasoning, coding support, testing, and documentation - paired with reflection on human responsibility and quality. Initial results indicate that the integrated approach supports hands-on competence development in AI-assisted engineering. Key observations highlight the need for agile teaching adaptations due to rapid tool evolution, the critical role of oral verification to ensure foundational learning. We close with lessons learned and recommendations for educators designing agile project-based curricula in the age of AI.

Anish Biswas, Kanishk Goel, Jayashree Mohan et al.

Agentic applications are LLMs that iteratively invoke external tools to accomplish complex tasks. Such tool-based agents are rapidly becoming the dominant paradigm for deploying language models in production. Unlike traditional single-turn inference, agentic workloads chain together multiple LLM calls and tool executions before producing a final response, creating a new performance bottleneck that manifests as increased latency in First Token Rendered (FTR) of the final answer. Through analysis of synthetic requests at production scale, we reveal three critical challenges: tool calls account for 30-80% of FTR latency, KV cache hit rates collapse despite substantial context reuse across iterations, and sequential orchestration wastes potential intra-request parallelism by sequentially executing LLM calls and tools. These bottlenecks stem from a design gap in which orchestrators and LLM engines operate as decoupled black boxes, preventing cross-layer optimizations. We present SUTRADHARA, a co-designed agentic inference system that integrates orchestration with LLM serving through a thin API enabling three optimizations: overlap tool execution with subsequent LLM prefill using tool-aware prompt splitting, streaming tool execution to dispatch tools incrementally during decode rather than waiting for complete output, and orchestrator-aware cache management that uses semantic hints to improve hit rates and reduce thrashing. Implemented on vLLM, SUTRADHARA reduces median FTR latency by 15% and end-to-end latency by 10% across workloads on A100 GPUs, demonstrating that co-design can systematically tame latency in agentic systems.

Weixing Zhang, Mario Herb, Martin Armbruster et al.

Despite Domain-Driven Design's proven value in managing complex business logic, a fundamental semantic expressiveness gap persists between generic modeling languages and tactical DDD patterns, causing continuous divergence between design intent and implementation. We envision a constraint-based tactical modeling environment that transforms abstract architectural principles into explicit, tool-enforced engineering constraints. At its core is a DDD-native metamodel where tactical patterns are first-class modeling primitives, coupled with a real-time constraint verification engine that prevents architectural violations during modeling, and bidirectional synchronization mechanisms that maintain model-code consistency through round-trip engineering. This approach aims to democratize tactical DDD by embedding expert-level architectural knowledge directly into modeling constraints, enabling small teams and junior developers to build complex business systems without sacrificing long-term maintainability. By lowering the technical barriers to DDD adoption, we envision transforming tactical DDD from an elite practice requiring continuous expert oversight into an accessible engineering discipline with tool-supported verification.

Gina Purnama Insany, Ranti Indriyani, Nadila Jannatul Ma’wa et al.

Indonesian people, especially the younger generation, often overlook the great potential of herbal leaves that are easily found around their homes. These leaves not only offer health benefits but also hold significant economic value. This research developed a system to classify 10 types of herbal leaves (<i>Annona muricata</i>, <i>Anredera cordifolia</i>, <i>Piper betle</i>, <i>Ocimum basilicum</i>, <i>Peperomia pellucida</i>, <i>Psidium guajava</i>, <i>Isotoma longiflora</i>, <i>Coleus scutellarioides</i>, <i>Ageratum conyzoides</i>, and <i>Syzygium polyanthum</i>) using artificial intelligence (AI). The study employed the Convolutional Neural Network (CNN) method and the You Only Look Once (YOLO) v11 algorithm, focusing on evaluating the performance of YOLOv11 in three variants, Nano, Small, and Medium. The results showed that the YOLOv11 Medium variant achieved the best performance, with the highest mAP50-95 value of 0.743 and mAP50 of 0.974 at the last epoch. The YOLOv11 Small variant outperformed Nano in precision (0.947 vs. 0.933) and mAP50 (0.973 vs. 0.972), while YOLOv11 Nano had slightly higher recall (0.921 vs. 0.906). Confusion Matrix results for YOLOv11 Medium showed precision (P) = 0.932, recall (R) = 0.928, mAP50 = 0.974, and mAP50-95 = 0.743. Based on these metrics, YOLOv11 Medium stood out as the best-performing variant, followed by Small and Nano. This research highlights the potential of AI technology to enhance the utilization of herbal leaves, which can provide broader health benefits and support the local economy.

Oussama Lahmar, Latifa Abdou, Imam Barket Ghiloubi et al.

The robust control of quadcopters is essential for maintaining stability and performance in dynamic environments. This paper examines the effectiveness of Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) for tuning LQR-PID controllers tailored for a quadcopter constrained to rotational degrees of freedom, aiming to enhance attitude stabilization and perform a comparative robustness analysis under various disturbances. Using PSO and GWO to optimize the LQR controller’s Q and R matrices, the study minimizes a cost function based on attitude error and control effort. The optimized controllers are evaluated in a Simulink environment with external perturbation forces; noise perturbations and sudden impulse disturbances are introduced via feedback vector perturbations to simulate real-world operational challenges. The results reveal distinct robustness characteristics: the PSO-optimized controller achieves faster convergence with higher sensitivity to disturbances, while the GWO-optimized controller performs better under extreme parameter variations. By providing a detailed comparison of these optimization techniques, the study offers valuable insights into selecting the most suitable method for robust and reliable quadcopter attitude control.

Muhammad Raees, Vassilis-Javed Khan, Konstantinos Papangelis

In our effort to implement an interactive customer segmentation tool for a global manufacturing company, we identified user experience (UX) challenges with technical implications. The main challenge relates to domain users' effort, in our case sales experts, to interpret the clusters produced by an unsupervised Machine Learning (ML) algorithm, for creating a customer segmentation. An additional challenge is what sort of interactions should such a tool support to enable meaningful interpretations of the output of clustering models. In this case study, we describe what we learned from implementing an Interactive Machine Learning (IML) prototype to address such UX challenges. We leverage a multi-year real-world dataset and domain experts' feedback from a global manufacturing company to evaluate our tool. We report what we found to be effective and wish to inform designers of IML systems in the context of customer segmentation and other related unsupervised ML tools.

Haotian Li, Yun Wang, Huamin Qu

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify consistently widely studied patterns, e.g., human-creator + AI-assistant, and newly explored or emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

Vahid Garousi, Zafar Jafarov, Aytan Mövsümova et al.

Artificial Intelligence (AI) tools such as GitHub Copilot and ChatGPT are increasingly used in software engineering (SE) for tasks such as code, test, and documentation generation. However, engineers often face uncertainty about when to trust, refine, or discard AI-generated artifacts. We present a pragmatic workflow, complemented by a four-quadrant decision model, that formalizes how developers iteratively prompt, inspect, refine, and, when needed, fall back to manual work. The workflow and decision model were derived from a grey literature review and field observations across three industrial settings in Türkiye and Azerbaijan. Two real-world scenarios demonstrate the workflow's practical value, showing how engineers navigate key decision points when using AI. Our approach offers lightweight, structured guidance to support more deliberate and quality-aware use of AI tools in everyday SE tasks.

Гончарова Ю.Ю., Дроботов А.В., Торубаров И.С. et al.

Подробно рассмотрены различные виды поверхностей для 3D-печати (3D столы) – стол с покрытием Ultrabase, полипропиленовая пластина, стол из пружинной стали с покрытием PEI, столы из ситаллового и боросиликатного стекала, перфорированные стеклянные. Проведено исследование по выявлению лучших адгезионных свойств деталей из материалов Nylon, PETG и PP к поверхностям стеклянного 3D стола, а также гладких и текстурированных столов из пружинной стали с покрытием PEI. Исследование проводилось для столов как с использованием специального адгезива, улучшающего сцепление поверхности 3D стола и образца, так и без него. С помощью микроскопа проведена фотофиксация поверхностей образцов, напечатанных на различных столах.

Hanxian Huang, Jishen Zhao

The increasing adoption of WebAssembly (Wasm) for performance-critical and security-sensitive tasks drives the demand for WebAssembly program comprehension and reverse engineering. Recent studies have introduced machine learning (ML)-based WebAssembly reverse engineering tools. Yet, the generalization of task-specific ML solutions remains challenging, because their effectiveness hinges on the availability of an ample supply of high-quality task-specific labeled data. Moreover, previous works overlook the high-level semantics present in source code and its documentation. Acknowledging the abundance of available source code with documentation, which can be compiled into WebAssembly, we propose to learn representations of them concurrently and harness their mutual relationships for effective WebAssembly reverse engineering. In this paper, we present WasmRev, the first multi-modal pre-trained language model for WebAssembly reverse engineering. WasmRev is pre-trained using self-supervised learning on a large-scale multi-modal corpus encompassing source code, code documentation and the compiled WebAssembly, without requiring labeled data. WasmRev incorporates three tailored multi-modal pre-training tasks to capture various characteristics of WebAssembly and cross-modal relationships. WasmRev is only trained once to produce general-purpose representations that can broadly support WebAssembly reverse engineering tasks through few-shot fine-tuning with much less labeled data, improving data efficiency. We fine-tune WasmRev onto three important reverse engineering tasks: type recovery, function purpose identification and WebAssembly summarization. Our results show that WasmRev pre-trained on the corpus of multi-modal samples establishes a robust foundation for these tasks, achieving high task accuracy and outperforming the state-of-the-art ML methods for WebAssembly reverse engineering.

Lovis Justin Immanuel Zenz, Erik Heiland, Peter Hillmann et al.

In this paper, we propose a method for aligning models with their realization through the application of model-based systems engineering. Our approach is divided into three steps. (1) Firstly, we leverage domain expertise and the Unified Architecture Framework to establish a reference model that fundamentally describes some domain. (2) Subsequently, we instantiate the reference model as specific models tailored to different scenarios within the domain. (3) Finally, we incorporate corresponding run logic directly into both the reference model and the specific models. In total, we thus provide a practical means to ensure that every implementation result is justified by business demand. We demonstrate our approach using the example of maritime object detection as a specific application (specific model / implementation element) of automatic target recognition as a service reoccurring in various forms (reference model element). Our approach facilitates a more seamless integration of models and implementation, fostering enhanced Business-IT alignment.

Mohsin Iqbal, Saravanan Karuppanan, Veeradasan Perumal et al.

Recently, fiber-reinforced polymers (FRP) have begun to be used for steel structure reinforcement, following decades of successful utilization for the reinforcement of concrete structures. However, rehabilitation of tubular joints with a crack at the interface of mating members using FRP has rarely been investigated. A tubular KT-joint having a semi-elliptical crack subjected to axial tensile load is explored in this study. The joint was simulated using the fracture tool of ANSYS Structural, and the effect of crack size, location, and FRP reinforcement on stress intensity factor (SIF) was evaluated. The numerical simulations show that FRP reinforcement reduces the SIF, decreases the likelihood of crack growth, and may increase the fatigue life. A 4–12% reduction per millimeter thickness of unidirectional FRP was recorded.

Umar Danjuma Maiwada, Kamaluddeen Usman Danyaro, Aliza Bt Sarlan et al.

In response to the rising demand for new and existing use cases of energy efficiency, the telecoms sector is going through a dramatic shift toward 5G technology. High data speeds, extensive coverage provided by dense base station deployment, higher capacity, improved Quality of Service (QoS), and extremely low latency are required for 5G wireless networks. New deployment methods, networking architectures, processing technologies, and storage solutions must be created to satisfy the anticipated service requirements of 5G technologies. These developments further increase the need to secure the security of 5G systems and their functionality as well as energy efficiency problems. Indeed, 5G system security is the target of intense efforts by developers and academics in this industry. Significant security concerns for 5G networks have been identified with extensive research. Attackers can make use of vulnerabilities like traffic and the flow base by introducing malicious code and performing other nefarious deeds to take advantage of the system. On 5G networks, attack techniques such as model node map (MNmap), power depletion assaults, and Man-in-the-Middle (MiTM) assaults can be effectively used. However, this study analyses 5G technology’s current energy efficiency problems. We recommend an unusual intrusion detection system (IDS) that makes use of Traffic Volume methods. Considering this investigation, we propose an enhancing training process by including statistical analysis on Distributed Denial-of-Service (DDoS) threats, which is how prior research recommended using OMNET and NS-3 on IDS for optimization. Additionally, the methodology for incorporating the suggested intrusion detection systems within a typical 5G architecture is presented by our research using NETSIM. The paper also offers a planned system’s correction method, providing a useful implementation after completing analysis.

Manyin Shi, Fang Luo, Hanping Ke et al.

Guided by system science, we propose a cognitive model based on graph theory and explore personalized recommendation algorithms based on a deep knowledge point tracking model by integrating the learning characteristics, prior knowledge, and learning ability of learners. Recommendation of the knowledge point is provided by combining the deep knowledge point tracking model and cognitive model, and personalized curriculum recommendation is provided by combining a knowledge point tracking model and graph theory. A dynamic personalized learning path is recommended by combining the knowledge point network and a student model. Then, teaching resources are recommended, and learning efficiency is improved.

Vanshika Bhalotia, Prathvi Shenoy

Solar energy is gaining immense significance as a renewable energy source owing to its environmentally friendly nature and sustainable attributes. Crystalline silicon solar cells are the prevailing choice for harnessing solar power. However, the efficiency of these cells is greatly influenced by their configuration and temperature. This research aims to explore the current–voltage (I−V) characteristics of individual, series, and parallel configurations in crystalline silicon solar cells under varying temperatures. Additionally, the impact of different temperature conditions on the overall efficiency and Fill Factor of the solar cell was analyzed. With the aid of a solar simulator and required conditions, the I−V characteristics of each configuration—individual, series, and parallel—were obtained. The solar panel was subjected to various temperature settings, and I−V characteristics were obtained for each configuration to calculate the maximum power and Fill Factor for each case. In addition to this, the results showed that the parallel configuration has a larger power output, followed by the individual and series configurations. Additionally, the temperature of the solar panel had a significant effect on the output power of the solar cells. The maximum output power is also affected by temperature variation. The Fill Factor, on the other hand, was observed to be dependent on the configuration but had no significant variation with respect to the temperature. The effect of solar irradiance was also observed in a configuration with a definite temperature. This research offers valuable insights into the ideal configuration and optimal temperature for achieving maximum efficiency in crystalline silicon solar cells. Hence, a definite configuration with optimum temperature yields maximum power output and helps in attaining maximum efficiency.

Istvan David, Dominik Bork, Gerti Kappel

The perception of the value and propriety of modern engineered systems is changing. In addition to their functional and extra-functional properties, nowadays' systems are also evaluated by their sustainability properties. The next generation of systems will be characterized by an overall elevated sustainability -- including their post-life, driven by efficient value retention mechanisms. Current systems engineering practices fall short of supporting these ambitions and need to be revised appropriately. In this paper, we introduce the concept of circular systems engineering, a novel paradigm for systems sustainability, and define two principles to successfully implement it: end-to-end sustainability and bipartite sustainability. We outline typical organizational evolution patterns that lead to the implementation and adoption of circularity principles, and outline key challenges and research opportunities.

Carlo A. Furia, Richard Torkar, Robert Feldt

There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations -- instead of potentially more insightful and robust causal relations. To support analyzing purely observational data for causal relations, and to assess any differences between purely predictive and causal models of the same data, this paper discusses some novel techniques based on structural causal models (such as directed acyclic graphs of causal Bayesian networks). Using these techniques, one can rigorously express, and partially validate, causal hypotheses; and then use the causal information to guide the construction of a statistical model that captures genuine causal relations -- such that correlation does imply causation. We apply these ideas to analyzing public data about programmer performance in Code Jam, a large world-wide coding contest organized by Google every year. Specifically, we look at the impact of different programming languages on a participant's performance in the contest. While the overall effect associated with programming languages is weak compared to other variables -- regardless of whether we consider correlational or causal links -- we found considerable differences between a purely associational and a causal analysis of the very same data. The takeaway message is that even an imperfect causal analysis of observational data can help answer the salient research questions more precisely and more robustly than with just purely predictive techniques -- where genuine causal effects may be confounded.

Wenxin Jiang, Mingyu Kim, Chingwo Cheung et al.

As innovation in deep learning continues, many engineers are incorporating Pre-Trained Models (PTMs) as components in computer systems. Some PTMs are foundation models, and others are fine-tuned variations adapted to different needs. When these PTMs are named well, it facilitates model discovery and reuse. However, prior research has shown that model names are not always well chosen and can sometimes be inaccurate and misleading. The naming practices for PTM packages have not been systematically studied, which hampers engineers' ability to efficiently search for and reliably reuse these models. In this paper, we conduct the first empirical investigation of PTM naming practices in the Hugging Face PTM registry. We begin by reporting on a survey of 108 Hugging Face users, highlighting differences from traditional software package naming and presenting findings on PTM naming practices. The survey results indicate a mismatch between engineers' preferences and current practices in PTM naming. We then introduce DARA, the first automated DNN ARchitecture Assessment technique designed to detect PTM naming inconsistencies. Our results demonstrate that architectural information alone is sufficient to detect these inconsistencies, achieving an accuracy of 94% in identifying model types and promising performance (over 70%) in other architectural metadata as well. We also highlight potential use cases for automated naming tools, such as model validation, PTM metadata generation and verification, and plagiarism detection. Our study provides a foundation for automating naming inconsistency detection. Finally, we envision future work focusing on automated tools for standardizing package naming, improving model selection and reuse, and strengthening the security of the PTM supply chain.

Shigeru KUROSAKI, Jyo SHIMURA, Syusaku YAMAJI et al.

A dedicated strain gage has been developed to measure the stress intensity factor of cracks. Conventional strain gages dedicated to stress intensity factor analysis have been made by combining multiple elements into a single gage. Therefore, it took time and effort to analyze the stress intensity factor. Therefore, in this research, we have developed a dedicated strain gage that can analyze the stress intensity factor more easily than before with only one gage of one element. The developed strain gage analysis formula uses the strain component εθ formula at the crack tip. We focused on the gage angle θ that brings the second term of the crack analysis strain component εθ close to 0. By setting the second term to 0, the stress intensity factor of the first term can be analyzed accurately in consideration of the second term. The developed gage has a donut shape with radii r1 and r2 and a width of 1 mm with the crack tip as the origin, and the gage grid angle has an angle from 0 ° to 52 °. A tensile test piece with a crack opening mode load was manufactured and a tensile experiment was conducted. The experimental value of the stress intensity factor was obtained within an error of ±10% from the theoretical value.