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The integration of artificial intelligence (AI) in manufacturing processes is a key driver of digital transformation in Industry 4.0. However, implementing AI in Zero-Defect Manufacturing (ZDM) remains challenging. Significant obstacles include data interoperability issues, redundancy in information flows, semantic inconsistencies, and inadequate adaptability to dynamic production contexts. This research proposes an ontological framework designed to structure and streamline the integration of AI and digital assets specifically for ZDM environments. The framework addresses interoperability problems between digital infrastructures, smart manufacturing systems, and defect-mitigation strategies by clearly defining ontological classes, attributes, and interrelationships. The ontology facilitates seamless semantic reasoning, reducing data redundancy and ambiguities while enhancing real-time responsiveness and decision-making capabilities. This research contributes to the standardization of AI integration in industrial environments by providing a unified conceptual model that enhances automation, interoperability, and resilience in intelligent manufacturing.

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An Ontological Framework Integrating Artificial Intelligence and Digital Assets for Zero-Defect Manufacturing

  • 会议论文

  • 发表日期：

    2026年

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  • Miguel A. Mateo-Casalí,
  • Andrés Boza,
  • Francisco Fraile

摘要

The integration of artificial intelligence (AI) in manufacturing processes is a key driver of digital transformation in Industry 4.0. However, implementing AI in Zero-Defect Manufacturing (ZDM) remains challenging. Significant obstacles include data interoperability issues, redundancy in information flows, semantic inconsistencies, and inadequate adaptability to dynamic production contexts. This research proposes an ontological framework designed to structure and streamline the integration of AI and digital assets specifically for ZDM environments. The framework addresses interoperability problems between digital infrastructures, smart manufacturing systems, and defect-mitigation strategies by clearly defining ontological classes, attributes, and interrelationships. The ontology facilitates seamless semantic reasoning, reducing data redundancy and ambiguities while enhancing real-time responsiveness and decision-making capabilities. This research contributes to the standardization of AI integration in industrial environments by providing a unified conceptual model that enhances automation, interoperability, and resilience in intelligent manufacturing.