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Advancing Manufacturing Intelligence: Decisional DNA-Based Methodology for Semi-automatic Manufacturing Environment
Abstract
The paper presents a novel smart information system tailored for semi-automatic manufacturing environments, utilizing Decisional DNA (DDNA) and the Set of Experience Knowledge Structure (SOEKS). The pre sented methodology addresses key challenges of semi-automatic settings such as predictive maintenance, data inconsistency, and optimization of human machine interaction. The methodology involves real-time data collection through IoT-enabled sensors integrated with DDNA-SOEKS, enabling effective decision support and process monitoring. Virtual Engineering Objects (VEO), Virtual Engineering Processes (VEP), and Virtual Engineering Factories (VEF) create a digital ecosystem for data representation, simulating real-world produc tion scenarios to optimize performance metrics such as machine downtime and Overall Equipment Effectiveness (OEE).
Citations
Authors (3)
-
Syed Imran Shafiq
- New Delhi University, India
-
Cesar Sanin
- AIH, Sydney, Australia
Cite as
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Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2025
- Bibliographic description:
- Shafiq S. I., Sanin C., Szczerbicki E.: Advancing Manufacturing Intelligence: Decisional DNA-Based Methodology for Semi-automatic Manufacturing Environment// / : , 2025,
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-981-96-5887-9_17
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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