Process Monitoring Using Machine Learning for Semi-Automatic Drilling of Rivet Holes in the Aerospace Industry

Publikationstyp
Conference Paper
Date Issued
2020-09
Sprache
English
Author(s)
Köttner, Lars  
Mehnen, Jan Philipp  
Romanenko, Denys 
Bender, S.  
Hintze, Wolfgang  
Institut
Produktionsmanagement und -technik M-18  
TORE-URI
http://hdl.handle.net/11420/8067
Start Page
497
End Page
507
Citation
Congress of the German Academic Association for Production Technology (WGP 2020)
Contribution to Conference
10th Congress of the German Academic Association for Production Technology, WGP 2020  
Publisher DOI
10.1007/978-3-662-62138-7_50
Scopus ID
2-s2.0-85165963266
The majority of aircraft rivet holes are drilled with semi-automatic and manually controlled, pneumatically driven machines as full automation is often unsuitable due to workspace restrictions. Lightweight materials of difficult machinability complicate drilling. This is particularly relevant when drilling stack materials, where the machining parameters are determined by the most difficult to machine material layer. To provide reliable rivet connections, drilling in multiple steps, use of minimum quantity lubrication as well as subsequent manual deburring and cleaning are indispensable. Newly developed electrically driven semi-automatic advanced drilling units (ADUs) enable intelligent process layouts and online condition monitoring by evaluating integrated sensor data. Additionally, process parameters can be adapted to suit each material in the stack.