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Vorhersage der Kernhärtenvarianz von industriell einsatzgehärtetem 18CrNi8
Author(s) -
Lingelbach Y.,
Waldenmaier T.,
Hagymasi L.,
Mikut R.,
Schulze V.
Publication year - 2022
Publication title -
materialwissenschaft und werkstofftechnik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.285
H-Index - 38
eISSN - 1521-4052
pISSN - 0933-5137
DOI - 10.1002/mawe.202100249
Subject(s) - variance (accounting) , nozzle , hardening (computing) , process (computing) , core (optical fiber) , materials science , computer science , mechanical engineering , engineering , composite material , accounting , layer (electronics) , business , operating system
Abstract To explain the variance in core hardness of 18CrNi8 nozzle bodies after industrial heat treatment, several data sources, including steel melt composition, sensor process data, and measurement errors, of five years are aggregated. In order to predict hardness variations caused by alloy composition, traditional physical models by Maynier are compared with data‐driven machine learning models, which show no advantage due to low data variability. Neither method can fully explain the visible drifts, which are better tracked by an alternative (i. e., filter model) that uses past measurements. Machine learning on features from heat treatment is not successful in predicting hardness change, presumably because the process is too stable. Finally, a large part of the variance is caused by the HV 1 measurement error.