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Development of a Holistic Method to Implement Artificial Intelligence in Manufacturing Areas

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Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1213))

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Abstract

Cognitive systems are finding their way into factories. Production plants are increasingly decentralized and optimize themselves independently within the Smart Factory. The overall conditions for using AI have improved dramatically in the last years: high computing power, available storage capacity by low costs and new methods of machine learning. More and more companies want to introduce AI in their production. At present, due to a lack of experience, there is a lack of systematic procedures for AI implementations in manufacturing. The possibilities of AI in manufacturing are extensive, but the wealth of experience is limited. The paper presents a method for the implementation of AI in companies, preferably in manufacturing. This method will support companies in the introduction of AI in manufacturing. The aim is to identify individual potentials for AI applications, generate a suitable use case for the company and implement it according the specific company environment.

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References

  1. BMWi: Technologieszenario “Künstliche Intelligenz in der Industrie 4.0”. Berlin: Bundesministerium für Wirtschaft und Energie (2019)

    Google Scholar 

  2. Rich, E., Knight, K., Nair, S.: Artificial Intelligence, 3rd edn. Tata McGraw-Hill, New Delhi (2009)

    Google Scholar 

  3. Searle, J.: Minds, brains, and programs: Behavioral and brain sciences. Bd. 3. Cambridge (1980)

    Google Scholar 

  4. Gardner, H., Davis, K., Christodoulou, J., Seider, S.: The Theory of Multiple Intelligences. In: Sternberg, R., Kaufman, B. The Cambridge Handbook of Intelligence, p. 485–503. Cambridge (2011)

    Google Scholar 

  5. Lunze, J.: Künstliche Intelligenz für Ingenieure, 2nd edn. Oldenbourg Verlag, München (2010)

    Book  Google Scholar 

  6. Kotu, V., Deshpande, B.: Predictive Analytics and Data Mining - Concepts and Practice with RapidMiner. Morgan Kaufmann, Waltham (2015)

    Google Scholar 

  7. Buxmann, P., Schmidt, H.: Grundlagen der Künstlichen Intelligenz und des Maschinellen Lernens. In: Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen. Springer-Verlag, Berlin, pp. 3–19 (2019)

    Google Scholar 

  8. Barnes, J.: Azure Machine Learning - Microsoft Azure Essentials. Microsoft Press, Washington (2015)

    Google Scholar 

  9. Kirste, M., Schurholz, M.: Entwicklungswege zur KI. In: Kunstliche Intelligenz - Technologie Anwendung Gesellschaft/ Wittpahl, Volker (Hrsg.). Springer-Verlag, Heidelberg, pp. 21–35 (2019)

    Google Scholar 

  10. Sultan, K., Ali, H., Zhang, Z.: Big data perspective and challenge in next generation networks. Future Internet 10, 56 (2018). MDPI J.

    Article  Google Scholar 

  11. Lee, K.: AI Superpowers. Houghton Mifflin Harcourt (2018)

    Google Scholar 

  12. Corea, F.: Applied Artificial Intelligence: Where AI can be Used in Business. SpringerBriefs in Complexity, Rome (2019)

    Book  Google Scholar 

  13. Doherty, C., Camina, S., White, K., Orenstein, G.: The Path to Predicitve Analytics and Machine Learning. O’Reilly Media, Sebastpool (2016)

    Google Scholar 

  14. Chapman, P., Clinton, J., Kerber, R., Khabaza, T.: The modeling agency (2000). https://the-modelingagency.com/crisp-dm.pdf. Accessed 20 Feb 2019

  15. Cleve, J., Lämmel, U.: Data Mining. München: Oldenbourg Wissenschaftsverlag (2014)

    Google Scholar 

  16. Olson, D., Delen, D.: Advanced Data Mining Techniques. Springer-Verlag, Heidelberg (2008)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Fraunhofer IAO, Fraunhofer Institute for Industrial Engineering IAO, Nobelstraße 12, 70569, Stuttgart, Germany

    Bastian Pokorni, Fabian Volz & Moritz Hämmerle

Authors
  1. Bastian Pokorni
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  2. Fabian Volz
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  3. Jan Zwerina
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  4. Moritz Hämmerle
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Corresponding author

Correspondence to Bastian Pokorni .

Editor information

Editors and Affiliations

  1. Institute for Advanced Systems Engineering, University of Central Florida, Orlando, FL, USA

    Tareq Ahram

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Pokorni, B., Volz, F., Zwerina, J., Hämmerle, M. (2021). Development of a Holistic Method to Implement Artificial Intelligence in Manufacturing Areas. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_1

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