# References

\[1]      Guo Z., Sha W.: Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network, Computational Materials Science 29 (2004) 12–28. <https://doi.org/10.1016/S0927-0256(03)00092-2>

\[2]      Ozerdem S., Kolukisa S.: Artificial Neural Network approach to predict mechanical properties of hot rolled, nonresulfurized, AISI 10xx series carbon steel bars, Journal of Materials Processing Technology 199 (2008) 437–439. <https://doi.org/10.1016/j.jmatprotec.2007.06.071>

\[3]      Feng S. et al.: "Using deep neural network with small dataset to predict material defects", Materials & Design 162 (2019) 300–310. <https://doi.org/10.1016/j.matdes.2018.11.060>

\[4]      Hart G. et al.: Machine learning for alloys, Nature Reviews Materials 6 (2021) 730–755. <https://doi.org/10.1038/s41578-021-00340-w>

\[5]      Total Materia AG: [www.totalmateria.com](http://www.totalmateria.com), accessed July 2022

\[6]      VDA 231-200: Werkstoffdatensatz - Spezifikation von Werkstoffen und Oberflächen in IT-Systemen / Material record, VDA, 2016

\[7]      Chen T., Guestrin C.: XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). Association for Computing Machinery, New York, NY, USA, 785–794. 2016. <https://doi.org/10.1145/2939672.2939785>

\[8]      Zou, M.; Jiang, W\.-G.; Qin, Q.-H.; Liu, Y.-C.; Li, M.-L. Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting. Materials 2022, 15, 5298. <https://doi.org/10.3390/ma15155298>

\[9]      S. Yan, D. Chen, S. Wang and S. Liu: Quality prediction method for aluminum alloy ingot based on XGBoost, 2020 Chinese Control and Decision Conference (CCDC), Hefei, China, 2020, pp. 2542-2547, doi: [10.1109/CCDC49329.2020.9164112](https://doi.org/10.1109/CCDC49329.2020.9164112).

\[10]    Deng, H., Zhou, Y., Wang, L. et al. Ensemble learning for the early prediction of neonatal jaundice with genetic features. BMC Med Inform Decis Mak 21, 338 (2021). <https://doi.org/10.1186/s12911-021-01701-9>

\[11]    Arik O.S., Pfister T.: TabNet: Attentive Interpretable Tabular Learning, Cornel University Archive (2020) <https://arxiv.org/abs/1908.07442>

\[12]    Sun J., Yang F.: Multi-factor Investment Model Based on TabNet, J. Phys. (2022) Conf. Ser. 2171 012057 <https://doi.org/10.1088/1742-6596/2171/1/012057>

\[13]    Metallic Materials Properties Development and Standardization (MMPDS) Handbook - 17, Battelle Memorial Institute, 2022

\[14]    Total Materia AG: System and Method for Processing Material Properties of Structural Materials, European Patent Office (2022) Application Number EP22181302.5


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