Options
Maschinelles Lernen zur Überwachung semi-automatischer Bohrprozesse im Flugzeugbau
Citation Link: https://doi.org/10.15480/882.16351
Publikationstyp
Doctoral Thesis
Date Issued
2026
Sprache
German
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2025-11-03
Institute
TORE-DOI
The large number of semi-automatically drilled holes in aircraft structural assembly provides a promising basis for the application of machine learning (ML) in data-based process monitoring using today's sensor technologies. This thesis investigates ML-based anomaly detection, process status, workpiece quality, and tool condition monitoring in semi-automatic drilling in order to identify the achievable prediction accuracies and optimal methods for the individual modeling steps.
Subjects
semi-automated drilling
machine learning
aircraft assembly
artifical intelligence
borehole quality
tool wear
DDC Class
670: Manufacturing
Funding(s)
SmartADU2020 (20Q11522)
Funding Organisations
Bundeswirtschaftsministerium
Loading...
Name
Denys_Romanenko_Maschinelles_Lernen_zur_Ueberwachung_semi_automatischer_Bohrprozesse_im_Fluzgzeugbau.pdf
Size
15.37 MB
Format
Adobe PDF