Hasil untuk "Engineering machinery, tools, and implements"

Himon Thakur, Armin Moin

The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.

Mark Looi

The rapid advance of Generative AI into software development prompts this empirical investigation of perceptual effects on practice. We study the usage patterns of 147 professional developers, examining perceived correlates of AI tools use, the resulting productivity and quality outcomes, and developer readiness for emerging AI-enhanced development. We describe a virtuous adoption cycle where frequent and broad AI tools use are the strongest correlates of both Perceived Productivity (PP) and quality, with frequency strongest. The study finds no perceptual support for the Quality Paradox and shows that PP is positively correlated with Perceived Code Quality (PQ) improvement. Developers thus report both productivity and quality gains. High current usage, breadth of application, frequent use of AI tools for testing, and ease of use correlate strongly with future intended adoption, though security concerns remain a moderate and statistically significant barrier to adoption. Moreover, AI testing tools' adoption lags that of coding tools, opening a Testing Gap. We identify three developer archetypes (Enthusiasts, Pragmatists, Cautious) that align with an innovation diffusion process wherein the virtuous adoption cycle serves as the individual engine of progression. Our findings reveal that organizational adoption of AI tools follows such a process: Enthusiasts push ahead with tools, creating organizational success that converts Pragmatists. The Cautious are held in organizational stasis: without early adopter examples, they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy. Policy itself does not predict individuals' intent to increase usage but functions as a marker of maturity, formalizing the successful diffusion of adoption by Enthusiasts while acting as a gateway that the Cautious group has yet to reach.

D. Popov, D. Mironov, Y. Tsench

This paper presents a systematic, multidimensional analysis of the historical and engineering evolution in the design of working tools for tillage machines, covering the period from the origins of agriculture to contemporary high-tech solutions. (Research purpose) The study identifies developmental patterns in design approaches, tracing the transition from artisanal production of basic agricultural implements, based on empirical knowledge, to the emergence of scientifically grounded methods of calculation and design, built on the advances in mechanics, materials science, and agrophysics. (Materials and methods) The research demonstrates that the introduction of new materials with improved performance characteristics, combined with the shift toward engineering calculations and virtual modeling (CAD/CAE/CAM), laid the foundation for the transition from universal to adaptive and smart design solutions. (Results and discussion) The study highlights the role of digitalization in improving the reliability, energy efficiency, and environmental sustainability of modern tillage machine components. It identifies key differences between the Soviet engineering school and international design methodologies. The paper underscores the contribution of scientific schools and research institutes to the development of soil-cutting theory and draft resistance calculations. Progress in the hardening of working tools is illustrated through the use of thermal and vibrational treatments, surfacing techniques, and coatings based on hard alloys. These technologies are presented not as isolated processes, but as integral components of the broader evolution in engineering design. Contemporary design trends are examined, including the application of digital twin technology, parametric geometry, precision agriculture technologies, and artificial intelligence. The study also addresses issues related to environmental sustainability, climate-adaptive engineering solutions, and sustainable agricultural practices. (Conclusions) The study concludes that an interdisciplinary approach is essential for the effective design of tillage implements, integrating agronomy, mechanical engineering, materials science, and digital technologies.

Jarief Farabi, Christos Mourouzidis, Pericles Pilidis

Hydrogen as fuel in civil aviation gas turbines is promising due to its no-carbon content and higher net specific energy. For an entry-level market and cost-saving strategy, it is advisable to consider reusing existing engine components whenever possible and retrofitting existing engines with hydrogen. Feasible strategies of retrofitting state-of-the-art Jet A-1 fuelled turbofan engines with hydrogen while applying minimum changes to hardware are considered in the present study. The findings demonstrate that hydrogen retrofitted engines can deliver advantages in terms of core temperature levels and efficiency. However, the engine operability assessment showed that retrofitting with minimum changes leads to a ~5% increase in the HP spool rotational speed for the same thrust at take-off, which poses an issue in terms of certification for the HP spool rotational speed overspeed margin.

Mohammed Saleh Alshaikh

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.

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.

Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

Mr P Rajapandian

ABSTRACT: Agriculture, as a labour-intensive field, relies significantly on efficient machinery to accelerate farming processes. Essential equipment like tractors, harvesters, tillage tools, and various implements play a vital role in modern agriculture. However, the high initial costs and expensive maintenance associated with these machines present substantial financial challenges for many farmers. To address this project, AgriAccess introduces an innovative agricultural machinery rental system designed to ease the financial burden on farmers. AgriAccess offers a user-friendly web dashboard and mobile app, equipping farmers with up-to-date information on farming techniques and available machinery. Through this platform, farmers can seamlessly rent essential equipment, allowing them to conduct farming activities from the comfort of their homes and reducing the costs of equipment ownership. This pioneering system not only enables timely and cost-effective crop harvesting but also allows individual farmers to rent out their machinery, creating an additional income stream. AgriAccess further serves as a marketplace for buying and selling used agricultural machinery, fostering a cooperative community among farmers. By focusing on the optimal utilization of available equipment, AgriAccess becomes a driving force for transforming traditional farming practices and promoting sustainable agriculture. Through efficient machinery usage and a collaborative platform, AgriAccess aims to enhance the sustainability and profitability of farming practices. Keywords: Smart farming, Easy equipment rentals, Helping farmers save money, Sharing farm tools, Farming made simple, Mobile and web access, Grow more with less, Modern agriculture, Community-powered farming, Tools when you need them

Prof. B.N. Tripathi, Prof. Pawan K. Sharma

Agricultural mechanization is broadly defined as the process of utilizing engineering and technological innovations such as farm tools, machinery, equipment, and power sources to perform agricultural operations more efficiently and effectively. According to the Food and Agriculture Organization (FAO), mechanization encompasses not only the use of tractors and harvesters but also implements for land preparation, irrigation, sowing, weeding, harvesting, processing, and storage. It is an essential component of agricultural modernization, enabling farmers to enhance productivity, reduce drudgery, save time, and ensure timeliness in crop operations.

Yusuf Öztürk

This study presents a comparative evaluation of machine learning (ML) and deep learning (DL) models for predictive maintenance (PdM) in small-scale industrial systems. A low-cost Arduino-based testbed equipped with vibration, temperature, and rotational speed sensors was developed to emulate real-world conditions. The primary focus of the study is the detailed implementation and analysis of a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). For benchmarking, two baseline models—Linear Regression and K-Nearest Neighbors (KNN)—were also implemented. According to the evaluation results, RNN-LSTM achieved the highest performance, with 95.31% accuracy, 0.047 MSE, 0.217 RMSE, 0.047 MAE, and 23.4% SMAPE. In comparison, Linear Regression and KNN yielded lower accuracies (92.30% and 93.27%) and higher error values (e.g., SMAPE of 58.7% and 41.2%). These findings confirm the superiority of RNN-LSTM in modeling temporal dependencies, while baseline models demonstrated limited generalization. Overall, the study shows that advanced DL models can be deployed on resource-constrained embedded systems, supporting the wider adoption of Industry 4.0 practices in small and medium-sized enterprises.

J. Muñoz, Mateo Del Gallo, Gerardo Minella et al.

The manufacturing industry is increasingly adopting Artificial Intelligence (AI)-based solutions to improve production planning and operational efficiency. This article reflects the work carried out in the context of the AIDEAS project. AIDEAS aims to develop AI solutions for the lifecycle of industrial equipment, within the manufacturing phase focusing on three of the key processes within the Supply Chain Management of procurement, fabrication and delivery. The AIProcurement Optimizer module supports purchasing decisions by considering supply constraints and cost targets, while AIFabrication Optimizer module improve production planning and scheduling through a combined approach of mathematical optimization and reinforcement learning. Finally, AI-Delivery Optimizer optimizes delivery logistics to reduce delays and transport costs. A holistic framework, AIDEAS Manufacturing Framework, is proposed that integrates all solutions, showing the connections between them and their workflow. The proposed framework undergoes testing in a real company from the inspection machinery industry through a structured implementation plan, highlighting both the benefits and challenges of adopting AI in small and medium enterprises. The findings underscore the role of AI in driving greater agility, sustainability, and resilience across manufacturing operations.

Axel Leonardo Huallpa-Palomino, Joseph Geanpierre Sanchez-Chiza, José Luis Álvarez-Arteaga et al.

The textile industry plays a critical role in the global economy. In this context, small and medium-sized manufacturing enterprises (SMEs) have shown notable growth, yet they continue to face significant operational challenges-particularly delays in order fulfillment. These delays are primarily attributed to recurrent machinery breakdowns and high rates of defective products. To address these issues, this study proposes an integrated model that combines tools from Planned Total Productive Maintenance (TPM) with an Internet of Things (IoT)-based approach, along with Autonomous TPM to reduce failure frequency, and Standard Work to minimize variability and product defects. The implementation of this model aims to enhance machine availability and standardize production processes. The effectiveness of the model was measured using the Production Efficiency indicator, which improved significantly from 66.6% to 78.1 %. Beyond solving the specific challenges of the case study company, this work aims to serve as a replicable framework for addressing operational inefficiencies and improving delivery performance across the textile sector.

Sangeetha Rajkumar, M. Ramachandran, Vimala Saravanan et al.

Agricultural implements are agricultural Human labor in activities that Reduce field crop yield Tools that can be used to improve is reapers, Traction, disc harrows, Cultivators, seed drills, Harrows, Spades, Baggage, Plows, and other agricultural Tools are very common. In Modern Agricultural Practices Agricultural implements play an important role play These are commercial and Widespread in organic farming are used. This Tools are for sowing, field preparation, Planting, threshing, and irrigation and are used for harvesting. Agricultural machinery industry or Agricultural engineering is a profession as part of the industry, it is in agriculture or other agriculture used tractors, Agricultural machinery and Manufacturing agricultural implements maintain. This branch is mechanically Considered part of the profession.Agricultural implements are agricultural to carry out procedures The necessary tools are: In today's farming operations Many agricultural implements are used. Agriculture means crops and livestock Production, Aquaculture, Aquaculture and food and food Forests for non-food products Includes. Seated man Agriculture at the Rise of Civilization A major development was Raised by this Cultivation of species is food Generated surpluses, which helped people live in cities. Humans are at least 105,000 grains years ago Although started to collect, New farmers are about 11,500 years ago They started planting. Sheep Goats, pigs, and cattle About 10,000 years ago were raised. World's lowest Plants are native to 11 regions and Cultivated as fodder. twentieth in century, large-scale Based on monocultures Industrial agriculture with dominated agricultural production.The weighted product method is a multi-criteria decision-making process is there are many alternatives, and based on several criteria we must determine the best alternative.DuPont India, Rallis India Limited, Nuziveedu Seeds Limited, Lemken India Agro Equipments Private Limited, Advanta Limited.Technical capability, Product quality capability, Delivery capability, Financial/cost capability from the result, it is seen that Lemken India Agro Equipments Private Limited is got the first rank where as Nuziveedu Seeds Limited is having the lowest rank

Ahad Ahmed Laskar

Agriculture is experiencing a period of technological change, driven by the addition of intelligent technologies into agricultural technology. The integration of smart systems into farm machinery has greatly improved soil fertility management and crop productivity. Advanced technologies such as sensors, IoT, AI, and precision agriculture tools enable real-time monitoring of critical soil parameters, leading to targeted interventions for improving soil health. Automated machinery with GPS and AI-driven algorithms ensures efficient seed placement, precise fertilizer application, and weed management, thereby minimizing resource wastage and environmental impact. Such insights based on data allow farmers to take appropriate decisions based on changing conditions and improve farming practices sustainably, but their large-scale adaptation can be impeded due to high implementation costs, issues with privacy over the data, and expertise over technicalities. But even these challenges are seen in light of increasing yield, input costs reduced, and sustainability-promoting benefits, thereby raising productivity and meeting the causes for environmental conservation and food security.

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.

Rudrajit Choudhuri, Ambareesh Ramakrishnan, Amreeta Chatterjee et al.

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students.

Katherine Melissa De la Torre, Cesar Gabriel Vilela, José Velásquez

Despite representing a significant percentage of economic growth, the food sector in Peru faces numerous challenges. Companies in this sector confront issues such as low efficiency in their production lines, high delay times, poorly maintained work areas, and high machinery downtime. This article explores solutions using Lean Manufacturing tools such as work study, 5S, Poka Yoke, and TPM with an IoT approach. Additionally, a pilot program will be implemented using the 8-step change management model to assess and quantify improvements in order to established performance parameters. By analyzing a company struggling with efficiency, it evaluates how Lean Manufacturing tools can enhance workflow and competitiveness. The aim of this research is to implement Lean Manufacturing tools to enhance the efficiency of a food sector company. To achieve this, key objectives must be considered, including optimizing workflow through process standardization and reducing machinery downtime through TPM - Planned Maintenance to improve efficiency by 3.58%. It is expected that the final outcome exceeds this percentage and that it improves over time, as the tools employed have the potential to further boost the company's efficiency.