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In today’s rapidly changing manufacturing environment, it is increasingly important to have dynamic process reconfiguration through adaptation control mechanisms. Digital Product Passports are also being developed and will become mandatory, such as for batteries in the European Union by 2027. Although this may initially require additional work, it can lead to synergies from the information obtained. By using \(\mathrm {CO_2~}\) values, e.g., from a Digital Product Passport of components, you can optimize the \(\mathrm {CO_2~}\) content of the end product to achieve a specific target and improve competitiveness. This can be achieved by adapting processes, such as choosing between high or low dynamics to influence overall energy consumption. A framework is necessary to extract specific information from DPP and make decisions for adaptation. This paper presents a framework architecture based on OPC UA and the AAS, accompanied by an illustrative example of a battery-packing handling process. The speed of the packing process is determined by the energy consumption values of the individual cells to reduce the total energy consumption value of the battery.

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Dynamic Process Reconfiguration Through Digital Product Passports: A Framework for Adaptive Production Control

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Samed Ajdinović,
  • Moritz Walker,
  • Rebekka Neumann,
  • Nicolai Maisch,
  • Michael Neubauer,
  • Armin Lechler,
  • Oliver Riedel

摘要

In today’s rapidly changing manufacturing environment, it is increasingly important to have dynamic process reconfiguration through adaptation control mechanisms. Digital Product Passports are also being developed and will become mandatory, such as for batteries in the European Union by 2027. Although this may initially require additional work, it can lead to synergies from the information obtained. By using \(\mathrm {CO_2~}\) values, e.g., from a Digital Product Passport of components, you can optimize the \(\mathrm {CO_2~}\) content of the end product to achieve a specific target and improve competitiveness. This can be achieved by adapting processes, such as choosing between high or low dynamics to influence overall energy consumption. A framework is necessary to extract specific information from DPP and make decisions for adaptation. This paper presents a framework architecture based on OPC UA and the AAS, accompanied by an illustrative example of a battery-packing handling process. The speed of the packing process is determined by the energy consumption values of the individual cells to reduce the total energy consumption value of the battery.