TU Darmstadt / ULB / TUbiblio

Tool Condition Monitoring and Tool Defect Detection for End Mills Based on High-Frequency Machine Tool Data

Fertig, Alexander ; Grau, Lukas ; Altmannsberger, Marius ; Weigold, Matthias (2021):
Tool Condition Monitoring and Tool Defect Detection for End Mills Based on High-Frequency Machine Tool Data.
In: MM Science Journal, 2021 (5), pp. 5160-5166. ISSN 1803-1269, e-ISSN 1805-0476,
DOI: 10.17973/MMSJ.2021_ 11_ 2021174,
[Article]

Abstract

In the context of increasing digitalization, machine tools have a decisive impact on the manufacturing of technically sophisticated products. The resulting large amount of available data opens up new opportunities for process monitoring and optimization. In this paper, a new in-process tool condition monitoring (TCM) approach for end mills is developed. Besides in-process wear determination, the presented approach also enables the early detection of tool manufacturing defects on end mills. By applying machine learning algorithms, high prediction accuracies can be achieved. The results allow the implementation of an in-process TCM system based on internal machine tool data.

Item Type: Article
Erschienen: 2021
Creators: Fertig, Alexander ; Grau, Lukas ; Altmannsberger, Marius ; Weigold, Matthias
Title: Tool Condition Monitoring and Tool Defect Detection for End Mills Based on High-Frequency Machine Tool Data
Language: English
Abstract:

In the context of increasing digitalization, machine tools have a decisive impact on the manufacturing of technically sophisticated products. The resulting large amount of available data opens up new opportunities for process monitoring and optimization. In this paper, a new in-process tool condition monitoring (TCM) approach for end mills is developed. Besides in-process wear determination, the presented approach also enables the early detection of tool manufacturing defects on end mills. By applying machine learning algorithms, high prediction accuracies can be achieved. The results allow the implementation of an in-process TCM system based on internal machine tool data.

Journal or Publication Title: MM Science Journal
Journal volume: 2021
Number: 5
Uncontrolled Keywords: Defect Classification, Internal Machine Tool Data, Machine Learning, Tool Condition Monitoring
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW)
Date Deposited: 10 Nov 2021 07:26
DOI: 10.17973/MMSJ.2021_ 11_ 2021174
Additional Information:

Special Issue: HSM 2021, 16th International Conference on High Speed Machining, October 26-27, 2021, Darmstadt, Germany

Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details