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  4. Machine Learning Pipeline for Predictive Maintenance in Polymer 3D Printing
 
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May 2, 2023
Journal Article
Title

Machine Learning Pipeline for Predictive Maintenance in Polymer 3D Printing

Abstract
Predictive maintenance is an enabler of achieving a sustainable production in 3D printing processes. The goal of this paper is to answer the question how to realize predictive maintenance for Fused Deposition Modeling (FDM) of Polyether Ether Ketone (PEEK). In a real-life use case, aging effects of the 3D printer's nozzle have a direct negative impact on product quality but are not detectable from the outside. For this purpose, a machine learning pipeline is built including the integration and preparation of sensor data as well as the training of a regression model to predict the nozzle's remaining useful lifetime.
Author(s)
Heymann, Henrik  orcid-logo
Fraunhofer-Institut für Produktionstechnologie IPT  
Schmitt, Robert H.  
Fraunhofer-Institut für Produktionstechnologie IPT  
Journal
Procedia CIRP  
Project(s)
AI-supported, generative 3D-Printing  
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Conference
Conference on Modeling of Machining Operations 2023  
Open Access
DOI
10.1016/j.procir.2023.03.058
10.24406/h-450635
File(s)
Full text.pdf (809.68 KB)
Rights
CC BY-NC-ND 4.0: Creative Commons Attribution-NonCommercial-NoDerivatives
Language
English
Fraunhofer-Institut für Produktionstechnologie IPT  
Keyword(s)
  • 3D Printing

  • FDM

  • Machine Learning

  • PEEK

  • Predictive Maintenance

  • Fused Deposition Modeling

  • Polyether Ether Ketone

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