EVENTS

Our events in the areas of Big Data and Research Innovation include a diverse set of topics such as Future, Strategy, Technology, Applications, and Management.

If you feel that your event or event series should be part of this event calendar, just contact us!

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Monday, November 20th, 2023 | 16:00 - 17:00 p.m.

Train Your Engineering Network - Self-acting anomaly detection and quality estimation for semi-automated drilling with machine learning methods

via Zoom

The presentation series “Train your engineering network” on diverse topics of Machine Learning addresses all interested persons at TUHH, from MLE partners as well as from the Hamburg region in general and aims at promoting the exchange of information and knowledge between these persons as well as their networking in a relaxed atmosphere. Thereby, the machine learning activities within MLE, TUHH and in the wider environment shall be made more visible, cooperations shall be promoted and also interested students shall be given an insight.

Sebastian Schibsdat & Denys Romanenko - Self-acting anomaly detection and quality estimation for semi-automated drilling with machine learning methods

Due to the high number of rivet holes per aircraft produced, automated process monitoring of the drilling process promises a significant reduction in manual inspection. Advances in sensor technology in new machine tools are greatly expanding the data base. Thus, self-learning can be applied to holistic process monitoring. In this presentation, the authors present approaches to anomaly detection and quality control in the drilling process. Supervised, semi-supervised and unsupervised methods were used for anomaly detection and compared with classical methods of quality control charts. In addition to engineered feature extraction, a new method was used to extract features using a CNN.

Lectures will be held online via Zoom on Mondays starting at 16:00 in the winter semester 2023 in English. General zoom link for all lectures: Link

Universität Hamburg
Adeline Scharfenberg
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Universität Hamburg
Adeline Scharfenberg
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein. 

Universität Hamburg
Adeline Scharfenberg
Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein.