Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

06-10-2020 | Production Process | Issue 5-6/2020

Production Engineering 5-6/2020

Optimization of the production processes of powder-based additive manufacturing technologies by means of a machine learning model for the temporal prognosis of the build and cooling phase

Journal:
Production Engineering > Issue 5-6/2020
Authors:
Paul Victor Osswald, Saad Kamal Mustafa, Christoph Kaa, Philip Obst, Martin Friedrich, Markus Pfeil, Dominik Rietzel, Gerd Witt
Important notes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

The increase in additive manufacturing production volume in recent years has led, not only, to a need for an increased level of productivity during the build job, but also the optimization of other crucial steps in each respective technologies process chain. With HP’s Multi-jet Fusion technology relatively recent entry (2014) into a seemingly stagnant scene of industrial scale powder-based additive manufacturing, it presented itself as an ideal candidate for post-processing optimization using methods of machine learning due to its faster print time. 66% of the Multi-jet Fusion production process, from build job preparation and nesting to delivery of finished parts is comprised of its cooling process step. This cooling process step can take anywhere from 3 to 30 h, depending on a number of factors. While speeding up cooling does not come into question when processing semi-crystalline polymers, such as polyamides, knowing the necessary cooling time with relatively high accuracy, becomes crucial. In this study, a machine learning model was created to predict a significantly more accurate cooling time using a number of build job parameters. The optimized cooling model was trained using the measured cooling times of varying build jobs as output and build job height, number of layers, packing density, number of parts and room temperature as inputs. The machine learning model predicts significantly more accurate cooling times than the manufacturer predictions. Furthermore, as with all machine learning models, it was shown that an increased number of data, resulted in more accurate predictions. The implementation of the optimized cooling model at BMW’s AM production facility leads to increased transparency, leaner production and a higher overall economic viability of AM technologies.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 5-6/2020

Production Engineering 5-6/2020 Go to the issue

Premium Partners

    Image Credits