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<p>Modern production heavily relies on data generation, automated processing, and decision-making to enhance quality and cost efficiency. Key elements for achieving fully autonomous, connected, and digital production, as envisioned by the Internet of Production (IoP), include data-driven inspection, machine-to-machine communication, and machine learning for process control. Current research focuses on concepts like cyber-physical systems, ontologies, digital shadows, and digital twins, often considering limited manufacturing processes, which restricts wider application due to high adaptation needs. This paper introduces a process-independent data model for describing industrial processes through interoperable data formats and a method for defining generic atomic production operations. This format captures data from general production runs to individual measurements, enabling comprehensive data representation and exchange across diverse manufacturing sectors, thus facilitating data sharing and digital value creation. The data model’s applicability is showcased within the IoP <i>Cluster of Excellence</i> using six distinct manufacturing processes.</p>

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Unifying data model of manufacturing processes for cross-domain collaboration in the Internet of Production

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

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  • Jiyoung Moon,
  • Lucia Ortjohann,
  • Philipp Niemietz,
  • Thomas Bergs,
  • Alexander Peitz,
  • Michael Emonts,
  • Christian Brecher,
  • Jana Sasse,
  • Yannik Lockner,
  • Malte Seefeldt,
  • Mauritius Schmitz,
  • Christian Hopmann,
  • Moritz Kröger,
  • Tamoghna Majumder,
  • Nilesh Thakare,
  • David Bailly,
  • Emad Scharifi

摘要

Modern production heavily relies on data generation, automated processing, and decision-making to enhance quality and cost efficiency. Key elements for achieving fully autonomous, connected, and digital production, as envisioned by the Internet of Production (IoP), include data-driven inspection, machine-to-machine communication, and machine learning for process control. Current research focuses on concepts like cyber-physical systems, ontologies, digital shadows, and digital twins, often considering limited manufacturing processes, which restricts wider application due to high adaptation needs. This paper introduces a process-independent data model for describing industrial processes through interoperable data formats and a method for defining generic atomic production operations. This format captures data from general production runs to individual measurements, enabling comprehensive data representation and exchange across diverse manufacturing sectors, thus facilitating data sharing and digital value creation. The data model’s applicability is showcased within the IoP Cluster of Excellence using six distinct manufacturing processes.