@article{Wegener_HTM2021,
 author = {Thomas Wegener and Alexander Liehr and Artjom Bolender and Sebastian Degener and Felix Wittich and Andreas Kroll and Thomas
Niendorf},
 doi = {https://doi.org/10.1515/htm-2021-0023},
 journal = {HTM Journal of Heat Treatment and Materials},
 language = {english},
 mrtnote = {peer, HartDrehen},
 number = {2},
 owner = {wittich},
 pages = {156 -- 172},
 timestamp = {2021.11.07},
 title = {Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties after hard
turning},
 volume = {77},
 year = {2022}
}

@inproceedings{WittichGMACI2021,
 author = {Felix Wittich and Andreas Kroll},
 booktitle = {31. Workshop Computational Intelligence},
 date = {2021},
 location = {Berlin},
 month = {25.-26. November},
 mrtnote = {nopeer, HartDrehen},
 organization = {GMA-FA 5.14},
 owner = {wittich},
 pages = {79-90},
 publisher = {KIT Scientific Publishing},
 timestamp = {2021.08.24},
 title = {Approximation der zulässigen Parametermenge bei der Bounded-Error-Schätzung durch ein Ray-Shooting-Verfahren},
 url = {https://doi.org/10.5445/KSP/1000138532},
 year = {2021}
}

@article{2021-FW-at-BE_TS_Methods,
 abstract = {In data-driven modeling besides the point estimate of the model parameters, an estimation of the parameter uncertainty is of great interest. For this, bounded error parameter estimation methods can be used. These are particularly interesting for problems where the stochastical properties of the random effects are unknown and cannot be determined. In this paper, different methods for obtaining a feasible parameter set are evaluated for the use with Takagi-Sugeno models. Case studies with simulated data and with measured data from a manufacturing process are presented.},
 author = {Felix Wittich and Andreas Kroll},
 journal = {at -- Automatisierungstechnik},
 journaltitle = {Computational Intelligence},
 language = {english},
 mrtnote = {peer,HartDrehen},
 number = {10},
 owner = {wittich},
 pages = {836--847},
 timestamp = {2020.11.06},
 title = {Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded
errors},
 url = {https://www.degruyter.com/document/doi/10.1515/auto-2020-0157/html},
 volume = {69},
 year = {2021}
}

@inproceedings{Schott_CIRP2020,
 address = {Sheffield, UK},
 author = {Christopher Schott and Felix Wittich and Andreas
Kroll and Thomas Niendorf},
 booktitle = {Procedia CIRP},
 doi = {10.1016/j.procir.2020.10.002},
 language = {english},
 mrtnote = {peer, HartDrehen, 3MA},
 owner = {wittich},
 pages = {1-4},
 timestamp = {2020.02.10},
 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{2020-FW-TM-BE_GPR_HardTurning,
 abstract = {In this article, two data-driven modeling approaches are investigated, which allow an explicit modeling of uncertainty. For this purpose, parametric Takagi-Sugeno multi-models with bounded-error parameter estimation and nonparametric Gaussian process regression are applied and compared. These models can for instance be used for robust model-based control design. As an application, the prediction of residual stresses during hard turning depending on the machining parameters and the initial hardness is considered.},
 author = {Felix Wittich and Lars Kistner and Andreas Kroll and Christopher Schott and Thomas
Niendorf},
 doi = {10.1515/teme-2020-0057},
 journal = {tm -- Technisches Messen},
 language = {english},
 mrtnote = {peer,HartDrehen},
 owner = {wittich},
 pages = {732-741},
 timestamp = {2020.10.16},
 title = {On data-driven nonlinear uncertainty modeling: Methods and application for control-oriented surface condition prediction in hard
turning},
 url = {https://doi.org/10.1515/teme-2020-0057},
 volume = {87},
 year = {2020}
}

@inproceedings{WittichSMC2019,
 address = {Bari, Italy},
 author = {Felix Wittich and Matthias Kahl and Andreas Kroll and Wolfgang Zinn and Thomas
Niendorf},
 booktitle = {IEEE International Conference on Systems, Man, and
Cybernetics (SMC 2019)},
 doi = {10.1109/SMC.2019.8914272},
 month = {06.-09. October},
 mrtnote = {peer,HartDrehen},
 organization = {IEEE},
 owner = {wittich},
 pages = {3235 -- 3240},
 timestamp = {2017.08.31},
 title = {On Nonlinear Empirical Modeling of Residual Stress
Profiles in Hard Turning},
 url = {https://doi.org/10.1109/SMC.2019.8914272},
 year = {2019}
}

@inproceedings{WittichGMA2019,
 address = {Dortmund},
 author = {Felix Wittich and Matthias Kahl and Andreas Kroll},
 booktitle = {29. Workshop Computational Intelligence},
 doi = {10.5445/KSP/1000098736},
 month = {28.-29. November},
 mrtnote = {nopeer,HartDrehen},
 organization = {GMA-FA 5.14},
 owner = {wittich},
 pages = {247-254},
 publisher = {KIT Scientific Publishing},
 timestamp = {2017.08.31},
 title = {Zur Schätzung zulässiger Parametermengen nichtlinearer Takagi-Sugeno-Multi-Modelle mit garantierten
Fehlerschranken},
 year = {2019}
}

@conference{WerkstoffWoche2019,
 author = {Felix Wittich},
 booktitle = {WerkstoffWoche 2019, Dresden},
 date = {2019.09.18},
 month = {September},
 mrtnote = {nopeer,HartDrehen},
 owner = {duerrbaum},
 timestamp = {2016.02.22},
 title = {Zur datengetriebenen Modellierung von
Eigenspannungen bei einem Hartdrehprozess},
 url = {https://www.werkstoffwoche.de/home/},
 year = {2019}
}

@mastersthesis{MA_wittich_2019,
 address = {Universität Kassel},
 author = {Felix Wittich},
 month = {Dezember},
 mrtnote = {education,HartDrehen},
 mrtnr = {227},
 owner = {kahl},
 school = {FG Mess- und Regelungstechnik},
 supervisor = {#ak#},
 timestamp = {2018.04.09},
 title = {Zur Schätzung zulässiger Parametermengen bei nichtlinearen Takagi-Sugeno-Multi-Modellen mit garantierten Fehlerschranken: Methoden und fertigungstechnische
Anwendung},
 type = {Masterarbeit},
 url = {https://mrt-pc1.mrt.maschinenbau.uni-kassel.de/MRT/Lehre/Aufgabenstellungen/2019-Wittich-MA-Set_Membership_Hartdrehen.pdf},
 year = {2019}
}

@inproceedings{WittichGMA2018,
 address = {Dortmund},
 author = {Felix Wittich and Matthias Gringard and Matthias Kahl and Andreas Kroll and Thomas Niendorf and Wolfgang
Zinn},
 booktitle = {28. Workshop Computational Intelligence},
 doit = {10.5445/KSP/1000085935},
 month = {29.-30. November},
 mrtnote = {nopeer,HartDrehen},
 organization = {GMA-FA 5.14},
 owner = {wittich},
 pages = {61 -- 81},
 publisher = {KIT Scientific Publishing},
 timestamp = {2017.08.31},
 title = {Datengetriebene Modellierung zur Prädiktion des Eigenspannungstiefenverlaufs beim
Hartdrehen},
 url = {https://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss/index.html},
 year = {2018}
}

@mastersthesis{BA_wittich_2018,
 address = {Universität Kassel},
 author = {Felix Wittich},
 month = {Februar},
 mrtnote = {education,HartDrehen},
 mrtnr = {218},
 owner = {kahl},
 school = {FG Mess- und Regelungstechnik},
 supervisor = {#ak#},
 timestamp = {2018.04.09},
 title = {Zur datengetriebenen Modellierung der Eigenspannungstiefenverteilung beim
Hartdrehen},
 type = {Bachelorarbeit},
 url = {https://mrt-pc1.mrt.maschinenbau.uni-kassel.de/MRT/Lehre/Aufgabenstellungen/2018-Wittich-BA-Identifikation_Hartdrehen.pdf},
 year = {2018}
}