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Sensor signal selection tool wear curve estimation and subsequent tool breakage prediction in a drilling operation.pdf (1.610Mb)
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Title
Sensor signal selection for tool wear curve estimation and subsequent tool breakage prediction in a drilling operation
Author
Duo, Aitor ccMondragon Unibertsitatea
Basagoiti, Rosa ccMondragon Unibertsitatea
ARRAZOLA, PEDRO JOSEMondragon Unibertsitatea
CUESTA ZABALAJAUREGUI, MIKEL ccMondragon Unibertsitatea
Research Group
Análisis de datos y ciberseguridad
Mecanizado de alto rendimiento
Published Date
2021
Publisher
Taylor & Francis
Keywords
Tool condition monitoring
Inconel 718
Drilling
Data mining ... [+]
Tool condition monitoring
Inconel 718
Drilling
Data mining
Machine learning [-]
Abstract
Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool ... [+]
Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can avoid costs resulting from that undesirable situation. This research work presents the results obtained in drilling process monitoring carried out on Inconel 718. Monitoring systems should be light and scalable. Following this idea, multiple sensors for external signal acquisition are used in this work (cutting forces, vibrations, and acoustic emissions) and several machine internal signals are collected. The main objective is to evaluate the capacity of each acquisition source for the reconstruction of the tool wear curve and subsequently detection of tool breakage. Given the difficulty of using all of these signals in a real system, the methodology used to analyse the data makes it possible to have a comparative analysis of the potential of each of these sources for tool wear monitoring during the drilling process. The results indicate cutting forces whether they come from internal signals or external signals can carry out this task accurately. At the same time of data acquisition, detailed tool wear measurements were added. [-]
URI
https://hdl.handle.net/20.500.11984/5621
Publisher’s version
https://doi.org/10.1080/0951192X.2021.1992661
ISSN
1362-3052
Published at
International Journal of Computer Integrated Manufacturing  Volume 35, Issue 2, 2022
Document type
Article
Version
Postprint – Accepted Manuscript
Rights
© 2021 Taylor & Francis
Access
Embargoed Access
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  • Articles - Engineering [483]

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