Jet Printing Quality ImprovementThrough Anomaly Detection UsingMachine Learning

Jet Printing Quality ImprovementThrough Anomaly Detection UsingMachine Learning

This case study examined emitted sound and actuated piezoelectric current in a solderpaste jet printing machine to conclude whether quality degradation could be detected with an autoencoder machine learning model. An autoencoder was used to detect anomalies in non-realtime that were defined asa diam...

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Bibliographic Details
Main Authors: Lind, Henrik, Janssen, Jacob
Format: Others
Language:English
Published: KTH, Skolan för industriell teknik och management (ITM) 2021
Subjects:
Jet Printing
Autoencoder
Anomaly Detection
Sound
Acoustic
Machine Learning
Solder Paste
Piezoelectric Current
Self Sensing
Engineering and Technology
Teknik och teknologier
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-306428

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-306428

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