© 2024 SOME RIGHTS RESERVED
The content of this web site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Germany License.
Arabian Journal for Science and Engineering, (), p. (in press) DOI:10.1007/s13369-023-08340-4
In the realm of additive manufacturing, process monitoring is typically realized through multi-sensor data fusion (MSDF) employing either classical methods like Kalman filtering or methods powered by artificial intelligence. In the latter approach, standard machine learning-based methods that involve handcrafted feature extraction and signal processing face challenges in generalization due to missing signal information and require domain expertise in data and feature selection. This study investigates MSDF in process monitoring from a signal-to-image encoding perspective within deep learning (DL), where intelligent fusion models are developed in different fusion levels, namely data, feature, and decision levels. Various signal imaging encoders, namely Gramian angular field, Markov transition field, and recurrence plots, are adopted and tested for fusion at these three levels. The fusion algorithms are implemented through three different DL-based classifiers, spanning different capacities and architectures recently established in this domain. The developed fusion frameworks are applied to the problem of process monitoring and anomaly detection in fused deposition modeling, utilizing a sensor dataset collected from a Delta 3D printer. Overall, the results indicate that the highest accuracies (up to 99.6%) can be achieved when employing feature-level fusion through a hybrid convolutional and recurrent deep model trained using recurrence plot anomaly images. Conversely, all data-level fusion models offer lower computational time at the cost of a slightly decreased accuracy. Considering the models’ response to various malfunctioning or glitching scenarios, once again, the feature-level fusion demonstrates outstanding stability and robustness, effectively attenuating considerable corruptions in the input signals without requiring model adjustments.
back
© 2024 SOME RIGHTS RESERVED
The content of this web site is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Germany License.
Please note: The abstracts of the bibliography database may underly other copyrights.
Ihr Browser versucht gerade eine Seite aus dem sogenannten Internet auszudrucken. Das Internet ist ein weltweites Netzwerk von Computern, das den Menschen ganz neue Möglichkeiten der Kommunikation bietet.
Da Politiker im Regelfall von neuen Dingen nichts verstehen, halten wir es für notwendig, sie davor zu schützen. Dies ist im beidseitigen Interesse, da unnötige Angstzustände bei Ihnen verhindert werden, ebenso wie es uns vor profilierungs- und machtsüchtigen Politikern schützt.
Sollten Sie der Meinung sein, dass Sie diese Internetseite dennoch sehen sollten, so können Sie jederzeit durch normalen Gebrauch eines Internetbrowsers darauf zugreifen. Dazu sind aber minimale Computerkenntnisse erforderlich. Sollten Sie diese nicht haben, vergessen Sie einfach dieses Internet und lassen uns in Ruhe.
Die Umgehung dieser Ausdrucksperre ist nach §95a UrhG verboten.
Mehr Informationen unter www.politiker-stopp.de.