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Improving Assembly Lines with Digital Twins: A Synthetic Case Study

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Advances in Artificial Intelligence – IBERAMIA 2024 (IBERAMIA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15277))

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Abstract

This study introduces a theoretical framework for the development and deployment of digital twins (DTs), with a focus on creating behavioral models using synthetic data. The aim is to enhance assembly line operations in smart manufacturing by improving process efficiency and product quality. The study details the types of synthetic data generated, the simulations conducted, and their outcomes, emphasizing significant improvements in operational processes. Key objectives include streamlining assembly processes, raising product standards, reducing machine downtime, and adjusting production to demand fluctuations. The research explores various categories of synthetic data, such as cycle times, machine performance metrics, product quality indicators, production volumes, and maintenance logs. Simulations with these datasets demonstrate the DT’s capability to predict and address production challenges effectively. The findings underscore the potential benefits of integrating DTs into manufacturing workflows, offering valuable insights for researchers and industry professionals. Additionally, the paper emphasizes the importance of validating these models with real-world data in future studies, including incorporating advanced AI features and verifying the methodology within actual manufacturing environments.

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Acknowledgments

The authors express their sincere appreciation for the financial of the MINISTRY OF SCIENCE AND INNOVATION (FPU17/04636). This work was partially supported with grant PID2021-1236730B-C31 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way making Europe”.

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Authors and Affiliations

  1. Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Valencia, Spain

    Maria Gabriela Juarez Juarez, Adriana Giret & Vicente Botti

Authors
  1. Maria Gabriela Juarez Juarez
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  2. Adriana Giret
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  3. Vicente Botti
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Correspondence to Maria Gabriela Juarez Juarez .

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Editors and Affiliations

  1. Universidade de Lisboa, Lisboa, Portugal

    Luís Correia

  2. Universidad de la República, Montevideo, Uruguay

    Aiala Rosá

  3. Universidad Complutense de Madrid, Madrid, Spain

    Francisco Garijo

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Juarez Juarez, M.G., Giret, A., Botti, V. (2025). Improving Assembly Lines with Digital Twins: A Synthetic Case Study. In: Correia, L., Rosá, A., Garijo, F. (eds) Advances in Artificial Intelligence – IBERAMIA 2024. IBERAMIA 2024. Lecture Notes in Computer Science, vol 15277. Springer, Cham. https://doi.org/10.1007/978-3-031-80366-6_3

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  • DOI: https://doi.org/10.1007/978-3-031-80366-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-80365-9

  • Online ISBN: 978-3-031-80366-6

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