Tool condition monitoring in drilling processes using anomaly detection approaches based on control internal data-Reference-Cited by-同舟云学术

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Tool condition monitoring in drilling processes using anomaly detection approaches based on control internal data

Published:2024 Issue: Volume:121 Page:216-221
ISSN:2212-8271
Container-title:Procedia CIRP
language:en
Short-container-title:Procedia CIRP

Author:

Reeber Tim,Henninger Jens,Weingarz Niklas,Simon Peter M.,Berndt Maximilian,Glatt Moritz,Kirsch Benjamin,Eisseler Rocco,Aurich Jan C.,Möhring Hans - Christian

Funder

Bundesministerium für Bildung und Forschung

BMBF Berlin

Publisher

Elsevier BV

Reference20 articles.

1. Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model;Javed;J. of Intelligent Manufacturing,2018

2. Reliability assessment of cutting tool life based on surrogate approximation methods;Salonitis;The Int. J. of Advanced Manufacturing Technology,2014

3. Tool wear monitoring using naive bayes classifiers;Karandikar;The Int. J. of Advanced Manufacturing Technology,2015

4. An investigation of tool wear using acoustic emission and genetic algorithm;Vetrichelvan;J. of Vibration and Control,2015

5. Incremental learning for online tool condition monitoring using ellipsoid artmap network model;Liu;Applied Soft Computing,2015

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spot-checking machine learning algorithms for tool wear monitoring in automatic drilling operations in CFRP/Ti6Al4V/Al stacks in the aircraft industry;Journal of Manufacturing Systems;2024-12

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