Towards an Advanced Artificial Intelligence Architecture through Asset Administration Shell and Industrial Data Spaces

  1. Legaristi Labajos, Jon 2
  2. Larrinaga, Felix 2
  3. Zugasti, Ekhi 2
  4. Cuenca, Javier 2
  5. Iñigo, Michel 4
  6. Kremer, Blanca 3
  7. Estepa, Daniel 3
  8. Ayuso, Mikel 1
  9. Montejo, Elena 5
  1. 1 Lortek, ICT Dept, Ordizia , Spain
  2. 2 Mondragon Unibertsitatea, Electronics and Computer Science Dept, Mondragon, Spain
  3. 3 Ikerlan, ICT Dept, Mondragon, Spain
  4. 4 Mondragon, S. Coop., Innovation and Technology Dept, Mondragon, Spain
  5. 5 Ideko, ICT and Automation Dept, Elgoibar, Spain
Mostrar afiliaciones +
Actas:
1st European Symposium on Artificial Intelligence in Manufacturing (ESAIM2023)

Año de publicación: 2023

Tipo: Aportación congreso

GOOGLE SCHOLAR lock_openeBiltegia editor

Resumen

This article develops an architecture for the implementation of Artificial Intelligence in the manufacturing value chain based on standard technologies and data spaces. The standards considered are IEC 63278 “Asset Administration Shell (AAS) for industrial applications” and DIN SPEC 27070:2020 – “Requirements and reference architecture of a security gateway for the exchange of industry data and services“ by IDSA. The architecture provides a data space that allows MONDRAGON industrial cooperatives to use data for the execution of advanced data analytics, Artificial Intelligence (AI) algorithms and interoperability between assets and IoT-platforms. The development of knowledge in this field allows, on the one hand, to optimise the consolidation of data as a strategic factor and, on the other hand, to increase collaboration between manufacturing companies, suppliers and technology providers. The article also explores specific Artificial Intelligence technologies with a wide application in industrial environments. In particular, the study has focused on research into Low/No Code, Explainability (XAI) tools and incremental learning algorithms. The contributions of this paper are summarised in 1) creating an IDS-AAS based architecture and data space that allows the exploitation of AI use cases, either by directly downloading models or by using AI as a service, 2) identifying useful AI tools for industry such as AutoML, No/Low code, XAI or incremental learning, 3) implementing a use case where different AI use alternatives are implemented.