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

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Intelligent Manufacturing and Mechatronics (iM3F 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 850))

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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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Acknowledgements

The authors would like to express their deepest gratitude to the Ministry of Higher Education (MoHE) and Universiti Malaysia Pahang Al-Sultan Abdullah for their generous financial support, which made this research possible. The funding provided through the Fundamental Research Scheme (FGRS) FRGS/1/2022/TK10/UMP/02/67 and the Universiti Malaysia Pahang Al-Sultan Abdullah’s Fundamental Research Grant RDU220317 was instrumental in facilitating the various aspects of this study, from the acquisition of materials to the conduction of experiments and analysis of data.

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Authors and Affiliations

  1. Faculty of Electrical and Electronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia

    Muhamad Ridzuan Radin Muhamad Amin & Abdul Nasir Abd. Ghafar

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia

    Norasilah Karumdin & Ahmad Noor Syukri Zainal Abidin

  3. Mechanical Engineering Section, Universiti Kuala Lumpur Malaysia France Institute, 43650, Bandar Baru Bangi, Selangor, Malaysia

    Muhammad Nur Farhan Saniman

Authors
  1. Muhamad Ridzuan Radin Muhamad Amin
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  2. Abdul Nasir Abd. Ghafar
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  3. Norasilah Karumdin
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  4. Ahmad Noor Syukri Zainal Abidin
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  5. Muhammad Nur Farhan Saniman
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Corresponding author

Correspondence to Abdul Nasir Abd. Ghafar .

Editor information

Editors and Affiliations

  1. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Wan Hasbullah Mohd. Isa

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Ismail Mohd. Khairuddin

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Mohd. Azraai Mohd. Razman

  4. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Sarah 'Atifah Saruchi

  5. School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Sze-Hong Teh

  6. Department of Computer Science, University of York, York, UK

    Pengcheng Liu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Amin, M.R.R.M., Abd. Ghafar, A.N., Karumdin, N., Abidin, A.N.S.Z., Saniman, M.N.F. (2024). Harnessing Machine Learning, Blockchain, and Digital Twin Technology for Advanced Robotics in Manufacturing: Challenges and Future Directions. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_5

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

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  • Publisher Name: Springer, Singapore

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