Implementation of advanced techniques in production and manufacturing sectors through support vector machine algorithm with embedded system

Rayner Alfred and Narender Chinthamu and T. Jayanthy and Elangovan Muniyandy and Tarun Kumar Dhiman and Nimmy John T. (2024) Implementation of advanced techniques in production and manufacturing sectors through support vector machine algorithm with embedded system. Measurement: Sensors, 33. pp. 1-7. ISSN 2665-9174

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Abstract

Our study set out to explore the integration of digital twins into manufacturing and production sectors with the aim of enhancing sustainability and operational efficiency. Employing a structured methodology, we utilized advanced 3D modeling techniques to create virtual representations of physical environments and products, forming the basis of our digital twin ecosystem. Through virtual product implementation, we simulated product behavior and performance, facilitating comprehensive testing and analysis before physical manufacturing. Leveraging artificial intelligence, particularly Support Vector Machine (SVM) optimization, we conducted performance analysis by processing data from interconnected sources, including social media platforms, to optimize digital transformation processes. Our results indicate that digital twins significantly enhance operational efficiency by detecting inefficiencies early, reducing production costs, and expediting time-to-market. Moreover, they contribute to sustainability efforts by minimizing resource wastage and environmental impact during manufacturing processes. The adoption of digital twins also fosters a culture of innovation and continuous improvement, enabling technological advancements and adaptability in corporate and industrial settings. In conclusion, our findings underscore the transformative potential of digital twins in driving positive change and optimizing industrial processes.

Item Type:	Article
Keyword:	Digital twin, Physical-virtual integration, Simulation, Digital transformation, Data interoperability, Machine learning, Predictive analytics, Sustainability optimization, Industry 4.0
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General) > T55.4-60.8 Industrial engineering. Management engineering T Technology > TS Manufactures > TS1-2301 Manufactures
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SITI AZIZAH BINTI IDRIS -
Date Deposited:	15 Aug 2025 10:49
Last Modified:	15 Aug 2025 10:49
URI:	https://eprints.ums.edu.my/id/eprint/44965

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