Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning
F. Grumbach, A. Müller, P. Reusch, S. Trojahn, Processes 11 (2023).
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Autor*in
Grumbach, Felix
;
Müller, Anna;
Reusch, Pascal;
Trojahn, Sebastian
;
Müller, Anna;
Reusch, Pascal;
Trojahn, SebastianErscheinungsjahr
Zeitschriftentitel
Processes
Band
11
Zeitschriftennummer
4
Artikelnummer
1267
eISSN
FH-PUB-ID
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Grumbach, Felix ; Müller, Anna ; Reusch, Pascal ; Trojahn, Sebastian: Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. In: Processes Bd. 11, MDPI AG (2023), Nr. 4
Grumbach F, Müller A, Reusch P, Trojahn S. Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. Processes. 2023;11(4). doi:10.3390/pr11041267
Grumbach, F., Müller, A., Reusch, P., & Trojahn, S. (2023). Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning. Processes, 11(4). https://doi.org/10.3390/pr11041267
@article{Grumbach_Müller_Reusch_Trojahn_2023, title={Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning}, volume={11}, DOI={10.3390/pr11041267}, number={41267}, journal={Processes}, publisher={MDPI AG}, author={Grumbach, Felix and Müller, Anna and Reusch, Pascal and Trojahn, Sebastian}, year={2023} }
Grumbach, Felix, Anna Müller, Pascal Reusch, and Sebastian Trojahn. “Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning.” Processes 11, no. 4 (2023). https://doi.org/10.3390/pr11041267.
F. Grumbach, A. Müller, P. Reusch, and S. Trojahn, “Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning,” Processes, vol. 11, no. 4, 2023.
Grumbach, Felix, et al. “Robustness Prediction in Dynamic Production Processes—A New Surrogate Measure Based on Regression Machine Learning.” Processes, vol. 11, no. 4, 1267, MDPI AG, 2023, doi:10.3390/pr11041267.
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