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

Aug 6, 2026

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Robots are examined as a transformative force in industrial settings, with a focus on current applications and emerging directions. The paper analyzes how machine learning can reshape industrial workflows to improve efficiency and output while exposing key obstacles, including data requirements and limits in model interpretability. It evaluates blockchain’s potential to strengthen security and transparency, along with risks from attacks and smart-contract weaknesses. It also reviews additive manufacturing constraints, new materials for robotic advancement, and research gaps driving future strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages1

Reading Time3 min

AccessFree to download

Abstract

Industrial robotics and present uses

Machine learning challenges

Blockchain security and transparency risks

Additive manufacturing constraints and future directions

Harnessing Machine Learning, Blockchain, and Digital Twin Technology for Advanced Roboticsin Manufacturing: Challengesand Future Directions Muhamad Ridzuan Radin Muhamad Amin , Abdul Nasir Abd. Ghafar , Norasilah Karumdin, Ahmad Noor Syukri Zainal Abidin, and Muhammad Nur Farhan Saniman 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 efﬁciencyand production while simultaneously highlighting the challenges ofdata 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. Keywords Machine learning · Blockchain technology · Robotics in manufacturing · Industrial automation · Advanced manufacturing technologies M. R. R. M. Amin · A. N. Abd. Ghafar (B) Faculty of Electrical and Electronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia e-mail: [abdnasir@umpsa.edu.my](abdnasir@umpsa.edu.my) N. Karumdin · A. N. S. Z. Abidin Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia M. N. F. Saniman Mechanical Engineering Section, Universiti Kuala Lumpur Malaysia France Institute, 43650 Bandar Baru Bangi, Selangor, Malaysia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024 W. H. Mohd Isa et al. (eds.), Intelligent Manufacturing and Mechatronics, Lecture Notes in Networks and Systems 850, [https://doi.org/10.1007/978-981-99-8819-8_5](https://doi.org/10.1007/978-981-99-8819-8_5) 61

FAQs

How does machine learning contribute to advanced robotics in manufacturing?

Machine learning is used to change industrial processes, improving efficiency and production outcomes. The paper also notes that success depends on sufficient data and limits in model interpretability.

What role does blockchain technology play, and what risks are highlighted?

Blockchain is presented as a way to improve industrial security and transparency. The paper also highlights hazards such as possible attacks and smart contract vulnerabilities.

Which constraints and future opportunities are discussed for additive manufacturing and materials?

Additive manufacturing—especially 3D printing—is analyzed in terms of challenges like printing speed, product quality, and material availability. The paper further points to potential progress using new materials such as bio-based polymers and 2D heterostructures for robotic systems.

Aug 6, 2026

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