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Erschienen in:
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2024 | OriginalPaper | Buchkapitel

Harnessing Machine Learning, Blockchain, and Digital Twin Technology for Advanced Robotics in Manufacturing: Challenges and Future Directions

verfasst von : Muhamad Ridzuan Radin Muhamad Amin, Abdul Nasir Abd. Ghafar, Norasilah Karumdin, Ahmad Noor Syukri Zainal Abidin, Muhammad Nur Farhan Saniman

Erschienen in: Intelligent Manufacturing and Mechatronics

Verlag: Springer Nature Singapore

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Abstract

This paper digs into robots’ revolutionary role in the industrial landscape, highlighting present uses and future trends while addressing ongoing problems. It investigates how machine learning is altering industrial processes, increasing efficiency and production while simultaneously highlighting the challenges of data needs and model interpretability. The evaluation investigates the promise of blockchain technology in enhancing industrial security and transparency, while also recognizing the hazards of possible attacks and smart contract vulnerabilities. The transformational influence of additive manufacturing, particularly 3D printing, is discussed, as well as the constraints connected with printing speed, product quality, and material availability. The study emphasizes the potential of new materials such as bio-based polymers and 2D heterostructures in the advancement of robotic systems. Despite these encouraging achievements, the assessment finds gaps in existing research and suggests future strategies for maximizing the potential of these technologies in the industrial industry.

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Nächstes Kapitel Humanizing Humanoids: An Extensive Review on the Potential of Prosthetic Robotic Arm with Integrated Monitoring System for Disabled People
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Metadaten
Titel
Harnessing Machine Learning, Blockchain, and Digital Twin Technology for Advanced Robotics in Manufacturing: Challenges and Future Directions
verfasst von
Muhamad Ridzuan Radin Muhamad Amin
Abdul Nasir Abd. Ghafar
Norasilah Karumdin
Ahmad Noor Syukri Zainal Abidin
Muhammad Nur Farhan Saniman
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Intelligent Manufacturing and Mechatronics

Print ISBN: 978-981-9988-18-1

Electronic ISBN: 978-981-9988-19-8

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-99-8819-8_5

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