M. Sc. Jens Decke

AI for Computationally Intelligent Systems (AI4CIS)

Telephone: +49 561 804-6037
Fax: +49 561 804-6022
Email: jens.decke[at]uni-kassel[dot]de

Site: Wilhelmshöher Allee 67
34121 Kassel

Room: 4107

Publications

[ 2022 ]

2022[ to top ]

Decke, J., Engelhardt, A., Rauch, L., Degener, S., Sajjadifar, S., Scharifi, E., Steinhoff, K., Niendorf, T., Sick, B.: Predicting flow stress behavior of an AA7075 alloy using machine learning methods Crystals. 9, 1–19 (2022). https://doi.org/10.3390/cryst12091281.
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This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^-1 to 0.1 s^-1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.
@article{decke2022predicting, abstract = {This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^{-1} to 0.1 s^{-1} were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.}, author = {Decke, Jens and Engelhardt, Anna and Rauch, Lukas and Degener, Sebastian and Sajjadifar, Seyedvahid and Scharifi, Emad and Steinhoff, Kurt and Niendorf, Thomas and Sick, Bernhard}, journal = {Crystals}, keywords = {itegpub}, number = 12, pages = {1–19}, publisher = {MDPI}, title = {Predicting flow stress behavior of an AA7075 alloy using machine learning methods}, volume = 9, year = 2022 }
%0 Journal Article %1 decke2022predicting %A Decke, Jens %A Engelhardt, Anna %A Rauch, Lukas %A Degener, Sebastian %A Sajjadifar, Seyedvahid %A Scharifi, Emad %A Steinhoff, Kurt %A Niendorf, Thomas %A Sick, Bernhard %D 2022 %I MDPI %J Crystals %N 12 %P 1--19 %R 10.3390/cryst12091281 %T Predicting flow stress behavior of an AA7075 alloy using machine learning methods %U https://www.mdpi.com/2073-4352/12/9/1281 %V 9 %X This work focuses on the prediction of hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. Data available are focusing on a novel hot forming process at different tool temperatures ranging from 24°C to 350°C to set different cooling rates after solution-heat-treatment. Isothermal uniaxial tensile tests in the temperature range from 200°C to 400°C and at strain rates ranging from 0.001 s^-1 to 0.1 s^-1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the well-known Zerilli-Armstrong model (Z-A) as an empirically based reference. Work focused on predicting single data points of curves that the model was trained on. Due to the novel way data were split with respect to training and testing data, it becomes possible to predict entire stress-strain curves which leads to a reduction in the number of required laboratory experiments, finally saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z-A model, the extreme Gradient Boosting model (XGB) showed the superior results, i.e., highest error reduction of 91% with respect to the Mean Squared Error.
Decke, J., Schmeißing, J., Botache, D., Bieshaar, M., Sick, B., Gruhl, C.: NDNET: A Unified Framework for Anomaly and Novelty Detection In: International Conference on Architecture of Computing Systems (ARCS). bll 197–210. Springer (2022). https://doi.org/10.1007/978-3-031-21867-5_13.
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We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.
@inproceedings{decke2022ndnet, abstract = {We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.}, author = {Decke, Jens and Schmeißing, Jörn and Botache, Diego and Bieshaar, Maarten and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {197–210}, publisher = {Springer}, title = {{NDNET}: {A} Unified Framework for Anomaly and Novelty Detection}, year = 2022 }
%0 Conference Paper %1 decke2022ndnet %A Decke, Jens %A Schmeißing, Jörn %A Botache, Diego %A Bieshaar, Maarten %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2022 %I Springer %P 197--210 %R 10.1007/978-3-031-21867-5_13 %T NDNET: A Unified Framework for Anomaly and Novelty Detection %X We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.