TY - GEN A1 - Jensch, Felix A1 - Eissing, Katharina A1 - Richard, Williams A1 - Trautmann, Marcus A1 - Yang, Yitong A1 - Dubinin, Sergej A1 - Härtel, Sebastian T1 - Improving the structural integrity of challenging to manufacture LPBF components with toolpath correction T2 - Materials Research Proceedings N2 - This work deals with the influence of optimised exposure strategies on the distortion and microstructure of components susceptible to overheating and warpage. Therefore, different distortion-prone specimen geometries of 316L were fabricated with the standard parameters, as well as with exposure strategies optimised by machine learning, which were generated using the AMAIZE software package. The manufactured samples were analysed with regard to distortion. The results of the distortion analysis were then linked with the results of the digital tomography from AMAIZE. Furthermore, components were manufactured that tend to overheat due to their geometry and orientation on the substrate plate. The influence of overheating during the LPBF process on the microstructure and porosity was investigated along the build-up direction by means of an EBSD analysis and a porosity analysis. With the presented approach for optimising the exposure strategy with AMAIZE, it could be shown that a successful production of distortion- prone components with a porosity of less than 1 % is possible in the first trial. KW - LPBF-Process, Machine Learning, Microstructural Investigation Y1 - 2024 UR - https://d21zja6o12zyp0.cloudfront.net/9781644903131.pdf U6 - https://doi.org/10.21741/9781644903131-12 SN - 2474-395X VL - 41 SP - 110 EP - 119 PB - Materials Research Forum LLC ER - TY - GEN A1 - Okolo, Chukwuemeka A1 - Eissing, Katharina A1 - Williams, Richard A1 - Jensch, Felix A1 - Fergani, Omar A1 - Härtel, Sebastian T1 - Investigation of the influence of AI-controlled process parameter adjustment on the mechanical properties of LBPF-manufactured parts T2 - Materials research proceedings N2 - This study investigates the influence of machine learning (ML) based process parameter adjustments on the microstructure, relative density, and mechanical properties of laser powder bed fusion (LPBF)-manufactured components, focusing on AlSi10Mg and Ti6Al4V. The ML algorithm optimizes the thermal history by adjusting laser power and exposure time at the vector level, ensuring consistent cooling and solidification dynamics. Microscopy revealed a refined and homogeneous microstructure in the optimized AlSi10Mg samples, with reduced grain size (4.92 µm compared to 6.37 µm in non-optimized samples). Relative density analysis showed a significant improvement for optimized samples, achieving consistent values across top, middle, and bottom sections of the specimen. Hardness measurements confirmed the homogenized mechanical properties, with more uniform and elevated hardness values observed in optimized samples. This study demonstrates that ML-based process optimization minimizes defects like porosity and microcracks, enabling improved mechanical performance and efficient process qualification for LPBF-manufactured parts. The findings underline the potential of AI-driven solutions for addressing complex geometrical and thermal challenges in LPBF process. KW - LPBF-process KW - Machine learning KW - Mechanical properties Y1 - 2025 SN - 978-1-64490-359-9 U6 - https://doi.org/10.21741/9781644903599-24 SN - 2474-395X VL - 54 SP - 218 EP - 227 PB - Materials Research Forum LLC CY - Millersville, PA ER -