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  4. Quality Prediction and Classification of Process Parameterization for Multi-Material Jetting by Means of Computer Vision and Machine Learning
 
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2024
Journal Article
Title

Quality Prediction and Classification of Process Parameterization for Multi-Material Jetting by Means of Computer Vision and Machine Learning

Abstract
Multi-Material Jetting (MMJ) is an additive manufacturing process empowering the printing of ceramics and hard metals with the highest precision. Given great advantages, it also poses challenges in ensuring the repeatability of part quality due to an inherent broader choice of built strategies. The addition of advanced quality assurance methods can therefore benefit the repeatability of part quality for widespread adoption. In particular, quality defects caused by improperly configured droplet overlap parameterizations, despite droplets themselves being well parameterized, constitute a major challenge for stable process control. This publication deals with the automated classification of the adequacy of process parameterization on green parts based on in-line surface measurements and their processing with machine learning methods, in particular the training of convolutional neural networks. To generate the training data, a demo part structure with eight layers was printed with different overlap settings, scanned, and labeled by process engineers. In particular, models with two convolutional layers and a pooling size of (6, 6) appeared to yield the best accuracies. Models trained only with images of the first layer and without the infill edge obtained validation accuracies of 90%. Consequently, an arbitrary section of the first layer is sufficient to deliver a prediction about the quality of the subsequently printed layers.
Author(s)
Reckert, Armin
Lang, Valentin
Weingarten, Steven  
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Johne, Robert  
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Klein, Jan-Hendrik
Ihlenfeldt, Steffen  
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Journal
Journal of manufacturing and materials processing  
Open Access
DOI
10.3390/jmmp8010008
Language
English
Fraunhofer-Institut für Keramische Technologien und Systeme IKTS  
Fraunhofer-Institut für Werkzeugmaschinen und Umformtechnik IWU  
Keyword(s)
  • computer vision

  • artificial intelligence

  • process monitoring

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