A systematic review of additive manufacturing-based remanufacturing techniques for component repair and restoration

Journal of Manufacturing Processes, Год журнала: 2023, Номер 89, С. 220 - 283

Опубликована: Фев. 2, 2023

Язык: Английский

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Big data, machine learning, and digital twin assisted additive manufacturing: A review

Materials & Design, Год журнала: 2024, Номер 244, С. 113086 - 113086

Опубликована: Июнь 25, 2024

Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast amounts of data that carry valuable information. Numerous research studies have been conducted to extract insights from AM and utilize it for optimizing various aspects such as process, supply chain, real-time monitoring. Data integration into proposed digital twin frameworks application machine learning techniques is expected play pivotal roles advancing future. In this paper, we provide an overview twin-assisted AM. On one hand, discuss domain highlight machine-learning methods utilized field, including material analysis, design optimization, process parameter defect detection monitoring, sustainability. other examine status current technical approach offer future developments perspectives area. This review paper aims present convergence big data, learning, Although there are numerous papers on additive others twins AM, no existing considered how these concepts intrinsically connected interrelated. Our first integrate three propose a cohesive framework they can work together improve efficiency, accuracy, sustainability processes. By exploring latest advancements applications within domains, our objective emphasize potential advantages possibilities associated with technologies

Язык: Английский

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Digital Twins in Industry 5.0

Research, Год журнала: 2023, Номер 6

Опубликована: Янв. 1, 2023

This work aims to explore the impact of Digital Twins Technology on industrial manufacturing in context Industry 5.0. A computer is used search Web Science database summarize First, background and system architecture 5.0 are introduced. Then, potential applications key modeling technologies discussd. It found that equipment infrastructure scenarios, embedded intelligent upgrade for a primary condition. At same time, can provide automated real-time process analysis between connected machines data sources, speeding up error detection correction. In addition, bring obvious efficiency improvements cost reductions manufacturing. reflects its application value subsequent through prospect. hoped this relatively systematic overview technical reference development improvement entire business Industrial X.0 era.

Язык: Английский

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Digital twins in additive manufacturing: a state-of-the-art review

The International Journal of Advanced Manufacturing Technology, Год журнала: 2024, Номер 131(1), С. 63 - 92

Опубликована: Фев. 1, 2024

Язык: Английский

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Stakeholders collaborations, challenges and emerging concepts in digital twin ecosystems

Information and Software Technology, Год журнала: 2024, Номер 169, С. 107424 - 107424

Опубликована: Фев. 14, 2024

Digital twin (DT) ecosystems are rapidly evolving, connecting many stakeholders, such as manufacturers, customers, and application platform providers. These require collaboration interaction between diverse actors to create value. This study delves into the of stakeholders within DT-focused ecosystems. research aims understand stakeholder DT ecosystems, identify potential challenges, provide insights for managing these stakeholders. It also seeks define ecosystem its implications both practice. A systematic literature review was conducted, supplemented by empirical evidence gathered from interviews with experts who were knowledgeable about ecosystem. The analyzed systems, roles, challenges ecosystem-focused development. identified various their roles in adding value a highlighted benefits collaboration, knowledge gain during system revealed technical non-technical encountered DTs, emphasizing importance standardization solution. new definition proposed, data-driven nature, interconnected creation, technology enablement. Stakeholder is pivotal each actor playing distinct role. Addressing especially through (OPC UA ISO 23247), can lead more efficient coherent provided this guide industries designing, developing, maintaining ensuring creation satisfaction. Future avenues that emphasize understanding involved deploy appropriate solutions suggested.

Язык: Английский

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Digital twin-based architecture for wire arc additive manufacturing using OPC UA

Robotics and Computer-Integrated Manufacturing, Год журнала: 2025, Номер 94, С. 102944 - 102944

Опубликована: Янв. 5, 2025

Язык: Английский

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A Comprehensive Review of AI-Based Digital Twin Applications in Manufacturing: Integration Across Operator, Product, and Process Dimensions

Electronics, Год журнала: 2025, Номер 14(4), С. 646 - 646

Опубликована: Фев. 7, 2025

Digital twins (DTs) represent a transformative technology in manufacturing, facilitating significant advancements monitoring, simulation, and optimization. This paper offers an extensive bibliographic review of AI-Based DT applications, categorized into three principal dimensions: operator, process, product. The operator dimension focuses on enhancing safety ergonomics through intelligent assistance, utilizing real-time monitoring artificial intelligence, notably human–robot collaboration contexts. process application concerns itself with optimizing production flows, identifying bottlenecks, dynamically reconfiguring systems predictive models simulations. Lastly, the product emphasizes applications focused improvements design quality, employing lifecycle historical data to satisfy evolving market requirements. categorization provides structured framework for analyzing specific capabilities trends DTs, while also knowledge gaps contemporary research. highlights key challenges technological interoperability, integration, high implementation costs emphasizing how digital twins, supported by AI, can drive transition toward sustainable, human-centered manufacturing line Industry 5.0. findings provide valuable insights advancing state art exploring future opportunities twin applications.

Язык: Английский

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Integrating Machine Learning Model and Digital Twin System for Additive Manufacturing

IEEE Access, Год журнала: 2023, Номер 11, С. 71113 - 71126

Опубликована: Янв. 1, 2023

Additive manufacturing is a promising process with diverse applications, but ensuring the quality and reliability of manufactured products key challenge. The digital twin has emerged as technology solution to address this challenge, allowing real-time monitoring control process. This paper proposes system framework for additive that integrates machine learning models, employing Unity, OctoPrint, Raspberry Pi monitoring. Particularly, utilizes models defect detection, achieving an Average Precision (AP) score 92%, specific performance metrics 91% defected objects 94% non-defected objects, demonstrating high efficiency. Unity client user interface also developed visualization, facilitating easy research article presents detailed description proposed its workflow implementation, interface. It demonstrates effectiveness integrated through case studies experimental results. main findings show met functional requirements effectively detects defects provides contributes growing field manufacturing, providing enhancing in manufacturing.

Язык: Английский

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Digital Twin Learning Ecosystem: A cyber–physical framework to integrate human-machine knowledge in traditional manufacturing

Internet of Things, Год журнала: 2024, Номер 25, С. 101094 - 101094

Опубликована: Янв. 29, 2024

As Industry 4.0 enablers, digital twins of manufacturing systems have led to multiple interaction levels among processes, systems, and workers across the factory. However, open issues still exist when addressing cyber–physical convergence in traditional small medium-sized enterprises. The problem for both operators existing infrastructure is how adapt knowledge increasing business needs plants that demand high efficiency, while reducing production costs. In this paper, a framework implements novel concept Digital Twin Learning Ecosystem presented. objective facilitate integration human-machine different industrial contexts eliminate technological workforce barriers. This adaptive approach particularly important meeting requirements help enterprises build their own interconnected Ecosystem. contribution work lies single twin learning scenarios can from scratch using light infrastructure, reusing common condition-based methods well-known by skilled rapidly flexibly integrate legacy resources non-intrusive manner. solution was tested real data milling machine currently operating induction furnace with maximum power 12 MW foundry plant. cases, proposed proved its benefits: first, providing augmented maintenance operations on second, improving efficiency approximately 9 percent.

Язык: Английский

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Advanced manufacturing and digital twin technology for nuclear energy*

Frontiers in Energy Research, Год журнала: 2024, Номер 12

Опубликована: Фев. 16, 2024

Advanced manufacturing techniques and digital twin technology are rapidly transforming the nuclear industry, offering potential to enhance productivity, safety, cost-effectiveness. Customized parts being produced using additive manufacturing, automation, robotics, while enables virtual modeling optimization of complex systems. These advanced technologies can significantly improve operational efficiency, predict system behavior, optimize maintenance schedules in energy sector, leading heightened safety reduced downtime. However, industry demands highest levels security, as well intricate processes operations. Thus, challenges such data management cybersecurity must be addressed fully realize industry. This comprehensive review highlights critical role with toward performance, minimize downtime, heighten ultimately contributing global mix by providing dependable low-carbon electricity.

Язык: Английский

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