Artificial Intelligence Optimisation of Laser Sintering of Ceramics: Towards a New Era of Additive Manufacturing

Selective laser sintering (SLS) enables the direct manufacturing of complex parts without the material loss common in conventional manufacturing methods. This thesis explores the integration of artificial intelligence (AI) in the laser sintering process of ceramics such as alumina, silicon carbide, calcium phosphate, zirconia, and regolith simulants, primarily composed of silicon dioxide. The objective is to optimize the parameters of additive manufacturing on a powder bed through selective laser melting/sintering to improve the quality and reproducibility of the produced parts, reduce production times, and accelerate the development of new ceramic materials.
The Instrumentation and Photonic Processes (IPP) research team at the ICube laboratory has developed an SLS prototype equipped with a 100 W CW 1090 nm laser and a galvanometric head for high speed and precision. The height and compression of the added layers are controlled by a Z-stage, roller, and blade. Following a joint study by INSERM and CNRS, and thanks to the work of recent PhD students and interns, this machine complies with the latest standards for protecting individuals from nanoparticles.
The thesis will begin by consolidating a database of sintering parameters previously obtained from regolith simulants. An in-depth analysis of the literature on laser sintering of ceramics, particularly focusing on density variations, cracks, and deformations, will enrich this database with new materials. The PhD student will then produce parts from various ceramics to populate the database. Sample characterization will primarily be conducted through compression tests and ceramography. Concurrently, AI algorithms will be adapted and applied to predict and adjust optimal parameters, such as laser power, scanning speed, and preheating temperature. Prediction algorithms, such as the highly promising new TabPFN foundation model, will be used to optimize and produce new samples.
This research will not be limited to parameter optimization. It will pave the way for the automation and customization of production, meeting the growing demands of Industry 4.0. It will also explore the potential of AI in designing new ceramic materials and integrating advanced functionalities, such as thermal or electrical conductivity.

In conclusion, this thesis lays the groundwork for a new era in the additive manufacturing of ceramics, where AI plays a central role in driving innovation and efficiency in production processes. The PhD student will work across two sites: Icam Strasbourg-Europe in Schiltigheim and ICube in Illkirch.

1. Zheng, Y., Zhang, K., Liu, T.T., Liao, W.H., Zhang, C.D., & Shao, H. (2019). Cracks of alumina ceramics by selective laser melting. *Ceramics International*. https://doi.org/10.1016/j.ceramint.2018.09.149
2. Picard, C., & Ahmed, F. (2024). Fast and Accurate Zero-Training Classification for Tabular Engineering Data. *arXiv preprint arXiv:2401.06948 [cs.CE]*. https://doi.org/10.48550/arXiv.2401.06948

3. Hollmann, N., Müller, S., Purucker, L., et al. (2025). Accurate predictions on small data with a tabular foundation model. *Nature, 637*, 319–326. https://doi.org/10.1038/s41586-024-08328-6
4. Hoo, S. B., Müller, S., Salinas, D., & Hutter, F. (2025). The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features. *arXiv preprint arXiv:2501.02945*. https://doi.org/10.48550/arXiv.2501.02945
5. Grossin, D., Montón, A., Navarrete-Segado, P., Özmen, E., Urruth, G., Maury, F., Maury, D., Frances, C., Tourbin, M., Lenormand, P., & Bertrand, G. (2021). A review of additive manufacturing of ceramics by powder bed selective laser processing (sintering / melting): Calcium phosphate, silicon carbide, zirconia, alumina, and their composites. *Open Ceramics, 5*, 100073. https://doi.org/10.1016/j.oceram.2021.100073
6. Sing, S. L., Yeong, W. Y., Wiria, F. E., Tay, B. Y., Zhao, Z., Zhao, L., Tian, Z., & Yang, S. (2017). Direct selective laser sintering and melting of ceramics: a review. *Rapid Prototyping Journal, 23*(3), 611-623. https://doi.org/10.1108/RPJ-11-2015-0178
7. Wang, K., Yin, J., Chen, X., Wang, L., Xiao, H., Liu, X., & Huang, Z. (2024). Advances on direct selective laser printing of ceramics: An overview. *Journal of Alloys and Compounds, 975*, 172821. https://doi.org/10.1016/j.jallcom.2023.172821

8. Ahlhelm, M., Richter, H. J., & Haderk, K. (2013). Selective Laser Sintering as an Additive Manufacturing Method for Manufacturing Ceramic Components. *J. Ceram. Sci. Tech., 04*(01), 33-40. DOI:10.4416/JCST2012-00024
9. Chabrol, G., Hayot, V., Ignjatovic, D., Moncoq, D., & Belut, E. (2024). Impact of nanoparticles during the experimental study of selected laser melting processes of regolith simulants for celestial applications. Proceedings of the 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 October 2024. IAC-24-A3-IP-129-x90991.

ICube Laboratory

The Engineering science, computer science and imaging laboratory

Created in 2013, the laboratory brings together researchers of the University of Strasbourg, the CNRS (French National Center for Scientific Research), the ENGEES and the INSA of Strasbourg in the fields of engineering science and computer science, with imaging as the unifying theme.

With around 650 members, ICube is a major driving force for research in Strasbourg whose main areas of application are biomedical engineering and the sustainable development.

Competence if possible in optics/optomechanics/optoelectronics for the implementation of the optical train in the  the current sintering system.
Competence in material characterisation by XRD, durometer, dilatometry, metallography, tensile/compression tests. 
Knowledge in laser sintering/melting on powder bed.
Experience in additive manufacturing.
Knowledge in ceramics.

Knowledge in artificial intelligence. Use of of TAB PNF.