Manufacturing Letters, Год журнала: 2024, Номер 41, С. 1312 - 1323
Опубликована: Окт. 1, 2024
Язык: Английский
IEEE Access, Год журнала: 2023, Номер 11, С. 101727 - 101749
Опубликована: Янв. 1, 2023
The Smart Factory has been a concept studied during the last decade that not standardized yet; for this reason, academy and industry have developed wide variety of new architectures describe integration elements digitization interconnection. present research aims to introduce architecture proposal migrating traditional (automation) smart (digitization) factories, implemented through open-source software. proposed is integrated, first time, by interconnection six main elements: cyber-physical systems, edge computing, artificial intelligence, cloud data analytics, cybersecurity; describes in detail their definitions, sub-elements, between elements, minimum requirements implementation. test factory was done scale pilot testing pick place process, where assembly wood pieces from geometric Tangram's puzzle required; includes six-degree-of-freedom robot arm, conveyor, vision system, storage area. case study conducted allowed four puzzles (fish, house, rocket, swan) were assembled with different batches pieces. implementation flexibility adaptability. final reports included status assembly, number assembled, stored, sequence, time. Similarly, development SCADA system asset control as well monitoring. KPIs process measured productivity (OTD) time tracking (ATCT TA) 16 tests, founding manufacturing cell fully integrated repeatability; SF represents an alternative small medium automated factories achieve digitization, it ready be tested more complex scenario.
Язык: Английский
Процитировано
26
SSRN Electronic Journal, Год журнала: 2024, Номер unknown
Опубликована: Янв. 1, 2024
This research paper explores the transformative possibilities arising from integration of ChatGPT, an advanced language model, into domain intelligent manufacturing. In face rapid changes in manufacturing landscape, there is increasing demand for adaptive and systems to elevate efficiency, productivity, decision-making processes. study investigates incorporation ChatGPT's or Bard cutting-edge natural processing capabilities various forefront aspects establish a novel paradigm The ChatGPT processes presents versatile approach tackle challenges seize opportunities within modern production systems. A pivotal aspect this lies augmenting human-machine collaboration factory. understanding facilitates seamless communication between human operators automated systems, fostering more intuitive responsive environment. Additionally, delves utilization predictive maintenance facilities. Through analysis historical data real-time information, can provide insights potential equipment failures, enabling proactive strategies that mitigate downtime optimize resource utilization. also application supply chain management. model's capacity process vast amounts textual contributes improved forecasting, inventory optimization, risk results resilient agile ecosystem capable adapting dynamic market conditions. Furthermore, role quality control defect detection. model analyze intricate patterns data, identifying anomalies defects with high degree accuracy. Integrating assurance ensures higher product quality, reducing waste, enhancing overall customer satisfaction. findings highlight revolutionize processes, propelling industry towards greater adaptability, competitiveness rapidly evolving global market.
Язык: Английский
Процитировано
11
IEEE Access, Год журнала: 2024, Номер 12, С. 64006 - 64049
Опубликована: Янв. 1, 2024
Anomaly detection is a critical task in ensuring the security and safety of infrastructure individuals smart environments.This paper provides comprehensive analysis recent anomaly solutions data streams supporting environments, with specific focus on multivariate time series various such as home, transport, industry.The aim to offer thorough overview current state-of-the-art techniques applicable these includes an examination publicly available datasets suitable for developing techniques.The survey designed inform future research practical applications field, serving valuable resource researchers practitioners.It not only reviews range methods, from statistical proximity-based those adopting deep learning-methods but also covers fundamental aspects detection.These include categorization anomalies, scenarios, challenges associated, evaluation metrics assessing techniques' performance.
Язык: Английский
Процитировано
6
Sensors, Год журнала: 2023, Номер 23(11), С. 5264 - 5264
Опубликована: Июнь 1, 2023
The Internet of Things (IoT) is gaining more and popularity it establishing itself in all areas, from industry to everyday life. Given its pervasiveness considering the problems that afflict today's world, must be carefully monitored addressed guarantee a future for new generations, sustainability technological solutions focal point activities researchers field. Many these are based on flexible, printed or wearable electronics. choice materials therefore becomes fundamental, just as crucial provide necessary power supply green way. In this paper we want analyze state art flexible electronics IoT, paying particular attention issue sustainability. Furthermore, considerations will made how skills required designers such circuits, features design tools characterization electronic circuits changing.
Язык: Английский
Процитировано
14
Procedia Computer Science, Год журнала: 2024, Номер 232, С. 327 - 336
Опубликована: Янв. 1, 2024
IoT is driving the digital transformation of companies into smart factories: data collected through technologies can provide powerful support to manufacturing operations management, making processes more efficient and leading improved productivity. To maximize value, turning raw actionable insights, Advanced Analytics algorithms be key opening novel opportunities for discovering hidden patterns in sensors. This review presents an overview current state-of-the-art integrated applications fields Quality, Maintenance, Production Planning & Control. Through a Systematic Literature Review, twenty-six articles were retrieved analyzed highlight points terms goals, employed techniques, use data. The results show that only few apply combination these techniques Control while on other hand Maintenance area highest maturity analytics since advanced data-related aspects are discussed selected studies. paper provides insights researchers managers sector interested unlocking value implementation one areas.
Язык: Английский
Процитировано
5
IEEE Transactions on Industrial Informatics, Год журнала: 2024, Номер 20(8), С. 10488 - 10498
Опубликована: Май 13, 2024
Compressed sensing (CS) for sensor-near vibration diagnostics represents a suitable approach the design of network-efficient structural health monitoring systems. This article presents solution analysis based on deep neural networks (DNNs) trained compressed data. The envisioned maintenance system consists network nodes orchestrated by very constrained centralizing unit. latter is equipped with microcontroller unit (MCU) that predicts state using aggregated information. As major contribution, DNN architectures are generated automatically from data through procedure inspired hardware-aware (HW) architecture search (NAS), called as HW-NAS-CS, which uniquely refined additional constraints consider both peculiarities CS parameters and limitation embedded devices. proposed has been validated two real-world SHM datasets damage identification eventually deployed low-end computing platform (the STM32L5 MCU). Results demonstrate DNNs combined adapted schemes can attain classification scores always above 90% even in case huge compression levels (higher than 64x): these performances significantly improve ones attained state-of-the-art approaches field, utmost advantage being portable
Язык: Английский
Процитировано
5
IEEE Access, Год журнала: 2024, Номер 12, С. 52110 - 52126
Опубликована: Янв. 1, 2024
In the rapidly evolving domain of e-commerce, effective warehouse management emerges as a critical factor for ensuring timely deliveries. This paper addresses Storage Location Assignment Problem (SLAP) in e-commerce warehouses, challenge intensified by varying product volumes and unpredictable demands. We introduce novel Intelligent (ISLA) method that utilizes advanced time series clustering algorithms specifically, Self-Organizing maps, dynamic warping-Based k-means, Agglomerative Hierarchical Clustering (AHC), to optimize order fulfillment enhance ware-house efficiency. By positioning items with similar demand patterns, our approach minimizes preparation time, reduces unnecessary movements, improves operational flows. Our empirical evaluation, based on real-world dataset from Kaggle, demonstrates superiority AHC efficiently grouping high-turnover items, evidenced higher silhouette scores. Applying this simulations across various picking strategies such s-shape, mid-point, discrete picking, zone batch we achieve significant efficiency improvements. Notably, ISLA results up 61% 69% gains under s-shape midpoint routing policies, respectively, outperforming traditional random ABC storage assignments. These not only highlight potential Artificial Intelligence (AI) revolutionizing operations but also bridge existing knowledge gap showcasing practical impactful application AI SLAP. research advances field smart logistics, emphasizing role AI-driven intelligent location assignment optimizing processes enhancing supply chain.
Язык: Английский
Процитировано
4
IEEE Access, Год журнала: 2024, Номер 12, С. 67537 - 67573
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
4
Journal of Applied Data Sciences, Год журнала: 2024, Номер 5(2), С. 455 - 473
Опубликована: Май 15, 2024
Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter paradigms in mechatronics sector. With advent 4.0, mechatronic systems have become increasingly sophisticated, highlighting critical need for advanced methodologies that are both efficient effective. This paper delves into confluence cutting-edge AI techniques, including machine learning (ML) deep (DL), with multi-agent (MAS) to enhance precision facilitate PdM context 4.0. Specifically, we explore use various ML models, Support Vector Machines (SVMs) Random Forests (RFs), DL architectures like Convolutional Neural Networks (CNNs) Recurrent (RNNs), which been effectively oriented analyses complex industrial data. Initially, study examines progress algorithms accelerate identification by leveraging data from system operations, sensors, historical trends. AI-enabled rapidly detects irregularities discerns fundamental causes, thereby minimizing downtime enhancing reliability efficiency. Furthermore, this underscores adoption AI-driven approaches, emphasizing prognostics predict Remaining Useful Life (RUL) machinery. capability allows strategic scheduling activities, optimizing resource use, prolonging lifespan expensive assets, refining management spare parts inventory. The tangible advantages employing showcased through case authentic implementations. highlights successful implementations, documenting real-world challenges such as integration issues interoperability, elaborates on strategies deployed navigate these obstacles. results demonstrate improved operational cost savings shed light pragmatic considerations solutions MAS applications. also navigates prospective research avenues applying domain setting stage ongoing innovation exploration transformative domain.
Язык: Английский
Процитировано
4
Springer series in advanced manufacturing, Год журнала: 2025, Номер unknown, С. 9 - 36
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0