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The design and management of Industry X.0 manufacturing systems is based on several simulation tools and digital models, used to predict and optimize the final performance. The lack of a seamless interoperability between such tools requires the development of a Digital Thread, a communication framework able to connect and synchronize digital models with the physical assets throughout their lifecycles. This study presents a novel Digital Thread based framework for the simulation and Virtual Commissioning of robotic systems, built on a Docker infrastructure integrated with Hadoop 3.2.0. It leverages AutomationML (AML) as a data exchange standard and the Robot Operating System (ROS) for distributed robotic control. The proposed architecture supports offline programming and facilitates the deployment of custom, multi-brand robotic systems, while enabling seamless integration of AML-based Digital Twins. The proposed methodology is validated on a large scale robotic manufacturing cell for aircraft fuselage assembly, demonstrating its capability in terms of performance validation, operational coordination, and optimization of production sequences. Preliminary tests underline the framework’s efficiency and effectiveness in bridging the gap between virtual simulations and physical deployment.

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Digital Thread Based Simulation Framework for Robotic Manufacturing Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Davide Ferrari,
  • Paolo Avanzi La Grotta,
  • Pietro Bilancia,
  • Roberto Raffaeli,
  • Marcello Pellicciari

摘要

The design and management of Industry X.0 manufacturing systems is based on several simulation tools and digital models, used to predict and optimize the final performance. The lack of a seamless interoperability between such tools requires the development of a Digital Thread, a communication framework able to connect and synchronize digital models with the physical assets throughout their lifecycles. This study presents a novel Digital Thread based framework for the simulation and Virtual Commissioning of robotic systems, built on a Docker infrastructure integrated with Hadoop 3.2.0. It leverages AutomationML (AML) as a data exchange standard and the Robot Operating System (ROS) for distributed robotic control. The proposed architecture supports offline programming and facilitates the deployment of custom, multi-brand robotic systems, while enabling seamless integration of AML-based Digital Twins. The proposed methodology is validated on a large scale robotic manufacturing cell for aircraft fuselage assembly, demonstrating its capability in terms of performance validation, operational coordination, and optimization of production sequences. Preliminary tests underline the framework’s efficiency and effectiveness in bridging the gap between virtual simulations and physical deployment.