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Towards the Digitalization of Additive Manufacturing

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Database and Expert Systems Applications - DEXA 2022 Workshops (DEXA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1633))

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Abstract

Additive manufacturing (AM) is a trending technology that is being adopted by many companies around the globe. The high level of product customization that this technology can provide, added to its link with key green targets such as the reduction of emissions or materials waste, makes AM a very attractive vehicle towards the transition to more adaptive and sustainable manufacturing. However, such a level of customization and this fast acceptance, raise new needs and challenges on how to monitor and digitalize the AM product life cycles and processes, which are essential features of a flexible factory that address adaptive and first-time-right manufacturing through the exploitation of knowledge gathered with the deep analysis of large amounts of data. How to organize and transfer such amounts of information becomes particularly complex in AM given not just its volume but also its level of heterogeneity. This work proposes a common methodology matching with specific data formats to solve the integration of all the information from AM processes in industrial digital frameworks. The scenario proposed in this work deals with the AM of metallic parts as a specially complex process due to the thermal properties of metals and the difficulties of predicting defects within their manipulation, making metal AM particularly challenging for stability and repeatability reasons but at the same time, a hot topic within AM research in general due to the large impact of such customized production in sectors like aeronautical, automotive, or medical. Also, in this work, we present a dataset developed following the proposed methodology that constitutes the first public available one of multi-process Metal AM components.

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Acknowledgments

figure a

This research has received funding from the European Union’s Horizon 2020 research and innovation programme under the project INTEGRADDE with Grant Agreement 820776. The authors want to thank the comments and fruitful discussions with all the members of the Artificial Intelligence and Data Analytics Lab (AIDA-Lab) of the Smart Systems and Smart Manufacturing (S3M) department and the people from the Advanced Manufacturing Processes department of the AIMEN Technology Centre.

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Authors and Affiliations

  1. Smart Systems and Smart Manufacturing, Artificial Intelligence and Data Analytics Laboratory, AIMEN Technology Centre, PI. Cataboi, 36418, Pontevedra, Spain

    Carlos González-Val, Christian Eike Precker & Santiago Muíños-Landín

Authors
  1. Carlos González-Val
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  2. Christian Eike Precker
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  3. Santiago Muíños-Landín
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Corresponding author

Correspondence to Santiago Muíños-Landín .

Editor information

Editors and Affiliations

  1. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  2. Technical University of Vienna, Vienna, Austria

    A Min Tjoa

  3. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

  4. Software Competence Center Hagenberg, Hagenberg, Austria

    Bernhard Moser

  5. (WU) Vienna University of Economics and Business, Vienna, Austria

    Alfred Taudes

  6. Johannes Kepler University of Linz, Linz, Austria

    Atif Mashkoor

  7. Johannes Kepler University of Linz, Linz, Austria

    Johannes Sametinger

  8. Software Competence Center Hagenberg, Hagenberg, Austria

    Jorge Martinez-Gil

  9. Software Competence Center Hagenberg, Hagenberg, Austria

    Florian Sobieczky

  10. Software Competence Center Hagenberg, Hagenberg, Austria

    Lukas Fischer

  11. Software Competence Center Hagenberg, Hagenberg, Austria

    Rudolf Ramler

  12. Pak-Austria Fachhochschule - Institute of Applied Sciences and Technology (PAF-IAST), Haripur, Pakistan

    Maqbool Khan

  13. Software Competence Center Hagenberg, Hagenberg, Austria

    Gerald Czech

Appendix A

Appendix A

Pieces on Metal Additive Manufacturing Open Repository

In this appendix, Fig. 6 shows some pictures of the physical specimens manufactured for the dataset are displayed to facilitate the visual understanding of the manufactured components.

  • The T-Coupon is a simple geometry that combines a curved wall with a rib manufactured with stainless steel as a first demonstrator of the capabilities of the presented methodology. Three unique specimens with different process parameters were manufactured.

  • The Jet Engine is a complex geometry that combines a cylindrical body with smaller subcomponents around it manufactured with stainless steel to showcase the use of the methodology with large real-world components. Two unique specimens were manufactured in different sizes.

  • The CC-Coupon is a simple geometry that combines two curved walls with three flat walls manufactured with stainless steel to demonstrate the capabilities of the presented methodology and formats to represent different processes and machines. These coupons were manufactured in four unique machines and locations, making a total of 28 different coupons created with completely different processes.

Fig. 6.
figure 6

Pictures of the pieces in the Metal Additive Manufacturing Open Repository

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González-Val, C., Precker, C.E., Muíños-Landín, S. (2022). Towards the Digitalization of Additive Manufacturing. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_14

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  • DOI: https://doi.org/10.1007/978-3-031-14343-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14342-7

  • Online ISBN: 978-3-031-14343-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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