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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 presented 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 production scenarios to optimize performance metrics such as machine downtime and Overall Equipment Effectiveness (OEE).

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Advancing Manufacturing Intelligence: Decisional DNA-Based Methodology for Semi-automatic Manufacturing Environment

  • 会议论文

  • 发表日期：

    2025年

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  • Syed Imran Shafiq,
  • Cesar Sanin,
  • Edward Szczerbicki

摘要

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 presented 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 production scenarios to optimize performance metrics such as machine downtime and Overall Equipment Effectiveness (OEE).