Hasil untuk "Engineering machinery, tools, and implements"

Lei Zhang

The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.

Mayuranath SureshKumar, Hanumanthrao Kannan

Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

Silvia Bertacchi

This paper presents the documentation, study activities, and possible applications of 3D digital models for the analysis and reconstruction of some examples of spheroidal stone projectiles—launched during the Sullan siege in 89 BC—now preserved in the Archaeological Park of Pompeii. The research proposes a methodology to derive best-fitting shapes that most closely adhere to the partially reconstructed image-based geometries. This allows a comparison with the circular ballistic impact traces still present on the ashlars of the northern city walls, as discovered by archaeologists about a hundred years ago. The results facilitate more precise ballistic calculations for the reconstruction of the elastic torsion weapons and their launching power.

Adinife Patrick Azodo, Tochukwu Canice Mezue, Idama Omokaro

Smartphone-based biosensors are emerging as transformative tools for personalized medicine and real-time health monitoring. This study critically examines recent advancements in mass-sensitive, optical, and electrochemical biosensing technologies, highlighting their application in detecting disease biomarkers. Beyond a summary of technological progress, this paper presents a strategic roadmap that addresses persistent barriers to clinical deployment, including sensor calibration inconsistencies, lack of interoperability, and limited scalability. The proposed framework leverages explainable artificial intelligence (AI) for enhanced diagnostic interpretation and outlines pathways for low-cost, scalable manufacturing using advanced nanomaterials. By bridging technical innovation with practical implementation, this work contributes a replicable model for developing equitable, reliable smartphone biosensors capable of expanding access to preventive and individualized healthcare worldwide.

Daniel Mendez, Paris Avgeriou, Marcos Kalinowski et al.

Empirical Software Engineering has received much attention in recent years and became a de-facto standard for scientific practice in Software Engineering. However, while extensive guidelines are nowadays available for designing, conducting, reporting, and reviewing empirical studies, similar attention has not yet been paid to teaching empirical software engineering. Closing this gap is the scope of this edited book. In the following editorial introduction, we, the editors, set the foundation by laying out the larger context of the discipline for a positioning of the remainder of this book.

Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio et al.

The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for generating domain models from textual descriptions. We use search-based methods to tune hyperparameters for a specific medical data model, resulting in a notable quality improvement over the baseline LLM. We then test the optimized hyperparameters across ten diverse application domains. While the solutions were not universally applicable, we demonstrate that combining hyperparameter tuning with prompt engineering can enhance results across nearly all examined domain models.

Virgil Gabriel Teodor

Drilling is one of the most widely used material machining processes, with applications in various industries such as machinery manufacturing, automotive, and aerospace. One of the factors that influence the quality and efficiency of the machining process is maintaining a constant cutting force.Maintaining the cutting force at a constant level during drilling is essential for ensuring dimensional precision, surface quality, tool durability, operational efficiency, and safety. Implementing advanced technologies such as automated control systems and force monitoring can significantly contribute to process optimization and risk reduction. This ensures high-quality production with reduced costs and increased productivity.In this study, the axial force distribution during the drilling of an aluminum alloy, 2024 T351, was determined, with measurements conducted for three combinations of machining parameters. A total of 27 holes were machined using drill bits with diameters of 6, 8, and 10 mm, cutting speeds of 50, 60, and 70 m/min, and feed rates of 0.1, 0.25, and 0.4 mm/rev.The axial force measurement was performed using a Kistler dynamometer available in the Manufacturing Engineering Department of the Faculty of Engineering at the “Dunărea de Jos” University of Galați.

Alicia Nahmad Vazquez, Soroush Garivani, Jean-Nicola Dackiw

Alfredo Iglesias-Rey, Carlos Alfonso López Hojas, F. Javier Martínez-Solano et al.

This paper demonstrates the synergistic use of engineering judgment and statistical/deep learning models, implemented through a four-step process using the software SAS Viya 4. Initial data filtering, input variable determination, and simultaneous application of RNN, LSTM, and GRU forecasting algorithms are conducted. Results are evaluated based on Battle of Water Demand Forecasting criteria, refining parameters iteratively for enhanced prediction accuracy. The methodology iteratively incorporates new data, streamlining neural network resolution.

Ghulam Ameer Mukhtar, Sana Shehzadi, Muhammad Moazzam Ali et al.

This study investigates the prospect of using rapid prototyping, particularly additive manufacturing, in Pakistan’s construction and manufacturing sectors, aiming to encourage R&D by the analysis of technical feasibility of this technology and collaboration between civil and industrial engineering. To solve this puzzle, we collected data from field experts, academia researchers, and license holders of this technology. Further, analytical hierarchy process (AHP), a sub-branch of multicriteria decision-making method (MCDM), was used to gauge the systematically by prioritizing selection criteria for solving the problem. AHP makes the methodical process more accurate and organized, which helped us to proposed a feasibility study for the technology’s success in Pakistan’s construction and manufacturing industries. The findings show a 79.4% probability, which indicates interaction among both engineering disciplines. Furthermore, a sensitivity analysis was conducted to enhance the dependability of the AHP model, which assists in sound decision making during ambiguous conditions. Apart from economic technical aspects, sustainability plays a very crucial role in the evaluation process. This text shows the environmental effects and sustainability implications associated with the assimilation of rapid prototyping technologies. This supports the integration of rapid prototyping in Pakistan, contributing to discussions on technological innovations in emerging nations. This will also lay a foundation for future interdisciplinary collaboration and technological enrichments in both engineering domains.

Mizuki TAKEDA, Kaiji SATO

There is a growing demand for robots that can assist the elderly to stand up independently. To this end, it is important for the robots to estimate the user's condition and provide appropriate assistance. In particular, the robots are required to be able to detect abnormal conditions and prevent accidents. However, accurate measurement of human posture requires a large number of sensors, making the system complex and difficult to handle. To estimate the state of a care robot user using a small number of sensors, we have proposed a method to calculate the position of the center of gravity as candidates. However, because the estimation was performed simply by using machine learning, it did not sufficiently consider how the center of gravity candidates changes depending on the state of the robot user. Therefore, it was necessary to measure not only data of normal operation but also data of abnormal states for binary classification. In this study, we propose and validate a method to detect abnormalities in standing without anomaly state data by analyzing how the center of gravity candidates tends to change over time in normal and abnormal standing. The analysis shows that the maximum value of the candidate center of gravity in the forward direction changes sharply when abnormal standing occurs. Therefore, anomaly detection was performed using the value of the second-order derivative of the maximum value of y of the candidate center of gravity, and abnormal standing was correctly detected.

Nadia Alshahwan, Mark Harman, Inna Harper et al.

In this paper we address the following question: How can we use Large Language Models (LLMs) to improve code independently of a human, while ensuring that the improved code - does not regress the properties of the original code? - improves the original in a verifiable and measurable way? To address this question, we advocate Assured LLM-Based Software Engineering; a generate-and-test approach, inspired by Genetic Improvement. Assured LLMSE applies a series of semantic filters that discard code that fails to meet these twin guarantees. This overcomes the potential problem of LLM's propensity to hallucinate. It allows us to generate code using LLMs, independently of any human. The human plays the role only of final code reviewer, as they would do with code generated by other human engineers. This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.

Marvin Wyrich, Justus Bogner

LinkedIn is the largest professional network in the world. As such, it can serve to build bridges between practitioners, whose daily work is software engineering (SE), and researchers, who work to advance the field of software engineering. We know that such a metaphorical bridge exists: SE research findings are sometimes shared on LinkedIn and commented on by software practitioners. Yet, we do not know what state the bridge is in. Therefore, we quantitatively and qualitatively investigate how SE practitioners and researchers approach each other via public LinkedIn discussions and what both sides can contribute to effective science communication. We found that a considerable proportion of LinkedIn posts on SE research are written by people who are not the paper authors (39%). Further, 71% of all comments in our dataset are from people in the industry, but only every second post receives at least one comment at all. Based on our findings, we formulate concrete advice for researchers and practitioners to make sharing new research findings on LinkedIn more fruitful.

Sonja Hyrynsalmi, Ella Peltonen, Fanny Vainionpää et al.

In the extant literature, there has been discussion on the drivers and motivations of minorities to enter the software industry. For example, universities have invested in more diverse imagery for years to attract a more diverse pool of students. However, in our research, we consider whether we understand why students choose their current major and how they did in the beginning decided to apply to study software engineering. We were also interested in learning if there could be some signs that would help us in marketing to get more women into tech. We approached the topic via an online survey (N = 78) sent to the university students of software engineering in Finland. Our results show that, on average, women apply later to software engineering studies than men, with statistically significant differences between genders. Additionally, we found that marketing actions have different impacts based on gender: personal guidance in live events or platforms is most influential for women, whereas teachers and social media have a more significant impact on men. The results also indicate two main paths into the field: the traditional linear educational pathway and the adult career change pathway, each significantly varying by gender

Ms. S. A. Deshpande, M. Deshpande

It is expected that the global value across all print and printed packaging will reach $843 billion in 2026. This is increasing after the pandemic-induced negative disruption in 2020. The packaging market has a growing share in it [1]. In Industry 4.0; the manpower, machinery, and printing processes are in the network digitally [2]. Accurate and precise data is controlling the production and digital twins are an important tool for increased overall efficiency and sustainability. Digital twins are connected from the sensors to the cloud-based software which can be accessed by all the stakeholders such as manufacturers, intermediate supply chain managers, wholesale clients, and end-use customers. Therefore, digital twins are an important part of Industry 4.0. In the print & packaging industry, due to increased automation, the investments in plant and machinery are increasing but customers expect cost reductions with an emphasis on quality and sustainability measures. The order quantities are reducing and demand for short-run customized products is increasing. The customers expect the printing companies to follow international standards diligently. There are several benefits of digital twins in the printing and packaging industry which result in saving time, manpower, cost and thus reducing the adverse impact on the environment in terms of effluents, use of power etc. thus increasing overall equipment efficiency and sustainability.

Andrés Cabieses-Chipoco, Edgar Jesús Lozano-Heredia

The textile industry is a major contributor to the global economy, particularly significant in Peru due to its rich history and traditional techniques. This sector not only provides substantial employment but also preserves cultural heritage and drives economic growth. Despite its importance, the Peruvian textile industry faces operational inefficiencies, particularly in machinery setup during product transitions, leading to high unproductive times and reduced outputs. Addressing these issues is critical for maintaining competitiveness and meeting industry standards. The proposed study introduced a Lean Manufacturing-based model to tackle these inefficiencies. The model integrated Systematic Layout Planning (SLP), 5S methodology, and Single-Minute Exchange of Die (SMED) techniques. SLP was employed to optimize plant layout, reducing transportation times and improving workflow. The 5S methodology aimed to create an organized and clean work environment, enhancing process stabilization. SMED focused on reducing machinery setup times, increasing production flexibility and responsiveness. The implementation of this model resulted in significant improvements. The Operational Efficiency Index increased from 83.4% to levels nearing the industry standard of 95%. Setup times were reduced by 49.80%, and transportation times between areas decreased by 53.81%. These improvements led to a notable economic impact, reducing annual losses and enhancing overall productivity and efficiency within the production process. Academically, this research contributed valuable insights into the application of Lean Manufacturing tools in the textile sector, a relatively underexplored area. Socioeconomically, the findings highlighted the potential for substantial gains in productivity and efficiency, promoting sustainable growth and competitiveness in the market

Kuda Tichiwangana, Fine Masimba, T. Zuva

The world has become a data driven society as many people have been roped into the internet of things, the use of internet compatible gadgets and machinery. The explosion of data becomes an essential tool for business to expand their scope to become competitive within their markets, understand and gain deeper insights into consumer behavior, and optimize business operations. SMEs are no exception, though they may face financial challenges among other things. Incorporating data analytics within an organization's functions may improve the business', decision making process, profitability, and market growth. This paper thus seeks to discover the adoption of data analytics by Small to Medium Enterprises and how it contributes to market growth. The study looks at the challenges and benefits that may be derived by Small to Medium Enterprises in implementing data analytics.

B. Hidalgo, O. M. Matamoros, J. Moreno et al.

Globalization has increased the demand for products, generating high competitiveness and significant production demands. In production processes, machinery breakdowns can affect planning and increase production costs. One way to prevent these breakdowns is through a preventive maintenance program. However, implementing these strategies requires time and money. In this context, simulation has become an extremely useful tool for analyzing production systems. This paper presents some future scenarios when implementing a preventive maintenance plan in a textile company. Using the iThink simulation software, the studied process was modeled, which allowed the establishment of future scenarios to understand how maintenance activities increase or decrease the productivity of the process.

Rohit Kumar

Abstract: In today's modern industrial economy, selecting suitable machinery and efficiently managing quality costs are critical for achieving sustainable growth and competitiveness. This research paper presents a comprehensive approach to optimizing machine selection and cost of quality (COQ) within industrial operations. The study commences by delineating the criteria for machine selection and determining their respective weights using the Analytic Hierarchy Process (AHP). Subsequently, the VIKOR method is applied to select the most suitable machine based on the established criteria and weights. Moreover, the paper explores the concept of COQ, underscoring its importance as a performance measurement tool for organizations. The research investigates various strategies for minimizing quality-related expenses and maximizing benefits, including defect prevention, quality assurance, and continuous improvement initiatives. A case study analysis, focusing on the selection between mechanical cutting CNC machines and laser cutting CNC machines, provides practical insights into the implementation of the proposed methodologies. Real data analysis of cost and quality metrics, coupled with formula-based calculations, offers valuable insights into the decision-making process. The research underscores the significance of market analysis and leveraging modern technology trends to inform machine selection decisions. Overall, the findings contribute to enhancing industrial efficiency and promoting economic growth by facilitating informed decision-making in machine selection and COQ management

Sachin Kumar, K. K., P. S. Aithal

Purpose: Businesses in all sectors, including the secondary industry, will turn to tech-business analytics as a crucial tool. Tech-Business Analytics' role in the secondary industrial sector is to support companies in making data-driven decisions that optimize their operations, boost productivity, and boost profitability. Businesses may optimize their supply chains by accessing data on suppliers, inventories, logistics, and other aspects to spot inefficiencies and areas for improvement. Organizations can use this information to reduce downtime and boost production to schedule maintenance in advance and predict when machinery and equipment will likely break. Examining data on product flaws, customer complaints, and other aspects can help firms improve their quality control systems by identifying root causes and implementing corrective measures. Studying data on consumer behaviour, industry trends, and other factors can help organizations optimize their sales and marketing activities and find chances for expansion and higher profitability. Design/Methodology/Approach: Businesses can use several processes in the tech-business analytics methodology to help them make decisions based on data in the secondary industry sector. This secondary industry sector can entail enhancing the effectiveness of the supply chain or decreasing equipment downtime. After identifying the issue, the necessary data must be gathered and prepared. Once the data is collected, it must be analyzed using statistical models and other analytical methods. This collected data might entail looking for relationships between multiple variables, spotting trends in consumer behaviour, or predicting outcomes using predictive models. Findings/Result: It is described in the article how tech-business analytics in the secondary industrial sector will have managed the growth itself from its inception to the present. The Tech-Business Analytics technique in the secondary industry sector offers a structured approach to problem-solving using data analysis to assist in better decision-making and improve business outcomes. Originality/Value: Exploring the evolutionary path of business analytics transforms into the advanced realm of technology-driven business analytics within the secondary industry sector. A generic architecture also examines 130 recently published Tech Business Analytics in Secondary Industry sector research projects for technical purposes. Tech-Business Analytics is a new field that applies ICCT-underpinning technologies in Tech-Business Analytics (TBA). TBA is intended to provide businesses with unprecedented opportunities for growth and innovation in secondary industry sectors. Paper Type: Exploratory research.