@article{engelhardt2023reliability,
  abstract = {Fracture surface analysis is of utmost importance with respect to structural integrity of metallic materials. This especially holds true for additively manufactured materials. Despite an increasing trend of automatization of testing methods, the analysis and classification of fatigue fracture surface images is commonly done manually by experts. Although this leads to correct results in most cases, it has several disadvantages, e.g., the need of a huge knowledge base to interpret images correctly. In present work, an unsupervised tool for analysis of overview images of fatigue fracture surface images is developed to support nonexperienced users to identify the origin of the fracture. The tool is developed using fracture surface images of additively manufactured Ti6Al4V specimens fatigued in the high-cycle-fatigue regime and is based on the identification of river marks. Several recording parameters seem to have no significant influence on the results as long as preprocessing settings are adapted. Moreover, it is possible to analyze images of other materials with the tool as long as the fracture surfaces contain river marks. However, special features like multiple origins or origins located in direct vicinity to the surface, e.g., caused by increased plastic strains, require a further tool development or alternative approaches.},
  author = {Engelhardt, Anna and Decke, Jens and Meier, David and Dulig, Franz and Ragunathan, Rishan and Wegener, Thomas and Sick, Bernhard and Niendorf, Thomas},
  doi = {https://doi.org/10.1002/adem.202300876},
  interhash = {1d9efbd65fd590eb56c2c12d9013bf67},
  intrahash = {c897cb5a3b7407d39bb824f9f8bf3802},
  journal = {Advanced Engineering Materials},
  number = 21,
  pages = 2300876,
  publisher = {Wiley},
  title = {On the Reliability of Automated Analysis of Fracture Surfaces Using a Novel Computer Vision-Based Tool},
  url = {https://onlinelibrary.wiley.com/doi/full/10.1002/adem.202300876},
  volume = 25,
  year = 2023
}

@article{meier2023reconstruction,
  abstract = {X‑ray diffraction crystallography allows non‑destructive examination of crystal structures.
Furthermore, it has low requirements regarding surface preparation, especially compared to
electron backscatter diffraction. However, up to now, X‑ray diffraction has been highly time‑
consuming in standard laboratory conditions since intensities on multiple lattice planes have to be
recorded by rotating and tilting. Furthermore, examining oligocrystalline materials is challenging
due to the limited number of diffraction spots. Moreover, commonly used evaluation methods
for crystallographic orientation analysis need multiple lattice planes for a reliable pole figure
reconstruction. In this article, we propose a deep‑learning‑based method for oligocrystalline
specimens, i.e., specimens with up to three grains of arbitrary crystal orientations. Our approach
allows faster experimentation due to accurate reconstructions of pole figure regions, which we did not
probe experimentally. In contrast to other methods, the pole figure is reconstructed based on only a
single incomplete pole figure. To speed up the development of our proposed method and for usage
in other machine learning algorithms, we introduce a GPU‑based simulation for data generation.
Furthermore, we present a pole widths standardization technique using a custom deep learning
architecture that makes algorithms more robust against influences from the experiment setup and
material.},
  author = {Meier, David and Ragunathan, Rishan and Degener, Sebastian and Liehr, Alexander and Vollmer, Malte and Niendorf, Thomas and Sick, Bernhard},
  codeurl = {https://git.ies.uni-kassel.de/digiwerk/pole-plots},
  doi = {10.1038/s41598-023-31580-1},
  interhash = {9ebd53e1681783bf8349601930bc161f},
  intrahash = {0181a195c4fcae4a6cc5f0af90863379},
  journal = {Scientific Reports},
  number = 1,
  pages = 5410,
  publisher = {Springer Nature},
  title = {Reconstruction of incomplete {X}-ray diffraction pole figures of oligocrystalline materials using deep learning},
  url = {https://www.nature.com/articles/s41598-023-31580-1},
  volume = 13,
  year = 2023
}

@article{Wegener_HTM2021,
  author = {Wegener, Thomas and Liehr, Alexander and Bolender, Artjom and Degener, Sebastian and Wittich, Felix and Kroll, Andreas and Niendorf, Thomas},
  doi = {https://doi.org/10.1515/htm-2021-0023},
  interhash = {f39818262d86287aafa15e734fad516f},
  intrahash = {8724667f1a37709d543e8fea50780e8f},
  journal = {HTM Journal of Heat Treatment and Materials},
  language = {english},
  mrtnote = {peer, HartDrehen},
  number = 2,
  owner = {wittich},
  pages = {156 -- 172},
  title = {Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties after hard turning},
  volume = 77,
  year = 2022
}

@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},
  doi = {10.3390/cryst12091281},
  interhash = {84be4cd3c699e31e03a6e5497f038f81},
  intrahash = {605f5f6f08fb5c0d632e009d917df18e},
  journal = {Crystals},
  number = 12,
  pages = {1--19},
  publisher = {MDPI},
  title = {Predicting flow stress behavior of an AA7075 alloy using machine learning methods},
  url = {https://www.mdpi.com/2073-4352/12/9/1281},
  volume = 9,
  year = 2022
}

@inproceedings{Schott_CIRP2020,
  address = {Sheffield, UK},
  author = {Schott, Christopher and Wittich, Felix and Kroll, Andreas and Niendorf, Thomas},
  booktitle = {Procedia CIRP},
  doi = {10.1016/j.procir.2020.10.002},
  interhash = {fc67dab5fd1e964fbcf06b2a5db0e859},
  intrahash = {23b6123e1e7cd2df46172a56128aa33d},
  language = {english},
  mrtnote = {peer, HartDrehen},
  owner = {wittich},
  pages = {1-4},
  title = {Prediction of near surface residual stress states for hard turned specimens using data driven nonlinear models},
  url = {https://www.sciencedirect.com/science/article/pii/S2212827121006429},
  volume = 101,
  year = 2021
}

@article{Wegener_HTM2021,
  author = {Wegener, Thomas and Liehr, Alexander and Bolender, Artjom and Degener, Sebastian and Wittich, Felix and Kroll, Andreas and Niendorf, Thomas},
  booktitle = {HTM Journal of Heat Treatment and Materials},
  interhash = {f39818262d86287aafa15e734fad516f},
  intrahash = {c2e84a6664a188ea5d59d3ce4800580f},
  language = {english},
  mrtnote = {peer, HartDrehen},
  note = {accepted},
  owner = {wittich},
  pubstate = {accepted},
  title = {Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties after hard turning},
  year = 2022
}

@inproceedings{WittichGMA2018,
  address = {Dortmund},
  author = {Wittich, Felix and Gringard, Matthias and Kahl, Matthias and Kroll, Andreas and Niendorf, Thomas and Zinn, Wolfgang},
  booktitle = {28. Workshop Computational Intelligence},
  doit = {10.5445/KSP/1000085935},
  interhash = {136412ec5da8a1bcea7adcfc2813f798},
  intrahash = {9a1758f9e31c386b20265b1f78bac448},
  month = {29.-30. November},
  mrtnote = {nopeer,HartDrehen},
  organization = {GMA-FA 5.14},
  owner = {wittich},
  pages = {61 -- 81},
  publisher = {KIT Scientific Publishing},
  title = {Datengetriebene Modellierung zur Prädiktion des Eigenspannungstiefenverlaufs beim Hartdrehen},
  url = {http://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss/index.html},
  year = 2018
}

@inproceedings{DLV+21,
  author = {Dingel, Kristina and Liehr, Alexander and Vogel, Michael and Degener, Sebastian and Meier, David and Niendorf, Thomas and Ehresmann, Arno and Sick, Bernhard},
  booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS},
  interhash = {3a961bf0b574af87c56e6b9d500fa954},
  intrahash = {c363e0439962503a3193fd7353faf095},
  maintitle = {2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems},
  note = {(accepted)},
  title = {{AI-Based On The Fly Design of Experiments in Physics and Engineering}},
  year = 2021
}