Tool condition monitoring in drilling processes using anomaly detection approaches based on control internal data
Authors:
- Tim ReeberTRTim Reeber
- Jens HenningerJHJens Henninger
- Niklas WeingarzNWNiklas Weingarz
- Peter M. SimonPSPeter M. Simon
- Maximilian BerndtMBMaximilian Berndt
- Moritz GlattMGMoritz Glatt
- Benjamin KirschBKBenjamin Kirsch
- Rocco EisselerRERocco Eisseler
- Jan C. AurichJAJan C. Aurich
- Hans - Christian MöhringHMHans - Christian Möhring
Open Access
- 1 February 2024
- journal article
- research article
- Published by Elsevier in Procedia CIRP
- Vol. 121, 216-221
Abstract
No abstract availableKeywords
Funding Information
- Bundesministerium für Forschung, Technologie und Raumfahrt (02P20A030)
- BMBF Berlin
This publication has 17 references indexed in Scilit:
- Review of tool condition monitoring in machining and opportunities for deep learningThe International Journal of Advanced Manufacturing Technology, 2020
- Tool condition monitoring techniques in milling process — a reviewJournal of Materials Research and Technology, 2020
- Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operationsJournal of Intelligent Manufacturing, 2019
- Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drillingJournal of Manufacturing Systems, 2017
- In-Process Tool Wear Detection of Uncoated Square End Mill Based on Electrical Contact ResistanceInternational Journal of Automation Technology, 2016
- Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble modelJournal of Intelligent Manufacturing, 2016
- Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network modelApplied Soft Computing, 2015
- Tool wear monitoring using naïve Bayes classifiersThe International Journal of Advanced Manufacturing Technology, 2014
- An investigation of tool wear using acoustic emission and genetic algorithmJournal of Vibration and Control, 2014
- Reliability assessment of cutting tool life based on surrogate approximation methodsThe International Journal of Advanced Manufacturing Technology, 2014