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Mikut, Ralf
Karlsruhe Institute of Technology
in Cooperation with on an Cooperation-Score of 37%
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Publications (5/5 displayed)
- 2024Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction
- 2023Temperature-based quality analysis in ultrasonic welding of copper sheets with microstructural joint evaluation and machine learning methodscitations
- 2022Material matters: predicting the core hardness variance in industrialized case hardening of 18CrNi8 [Vorhersage der Kernhärtenvarianz von industriell einsatzgehärtetem 18CrNi8]
- 2021CAD-to-real: enabling deep neural networks for 3D pose estimation of electronic control unitscitations
- 2019Towards DeepSpray: Using Convolutional Neural Network to post-process Shadowgraphy Images of Liquid Atomization
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article
Material matters: predicting the core hardness variance in industrialized case hardening of 18CrNi8 [Vorhersage der Kernhärtenvarianz von industriell einsatzgehärtetem 18CrNi8]
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.