Computational methods for the detection of wear and damage to milling tools br
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In: JOURNAL OF MANUFACTURING PROCESSES, Vol. 82, 10.2022, p. 78-87.
Research output: Contribution to journal › Article › Research › peer-review
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TY - JOUR
T1 - Computational methods for the detection of wear and damage to milling tools br
AU - Ninevski, Dimitar
AU - Thaler, Julia
AU - O'Leary, Paul
AU - Klunsner, Thomas
AU - Mucke, Manfred
AU - Hanna, Lukas
AU - Teppernegg, Tamara
AU - Treichler, Martin
AU - Peissl, Patrick
AU - Czettl, Christoph
PY - 2022/10
Y1 - 2022/10
KW - Condition monitoring
KW - Milling tool damage
KW - Time-series sensor data
U2 - 10.1016/j.jmapro.2022.07.030
DO - 10.1016/j.jmapro.2022.07.030
M3 - Article
VL - 82
SP - 78
EP - 87
JO - JOURNAL OF MANUFACTURING PROCESSES
JF - JOURNAL OF MANUFACTURING PROCESSES
SN - 1526-6125
ER -