Investigation of the influence of AI-controlled process parameter adjustment on the mechanical properties of LBPF-manufactured parts
Chukwuemeka Okolo, Katharina Eissing, Richard Williams, Felix Jensch, Omar Fergani, Sebastian Härtel
Abstract. 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.
Keywords
LPBF-Process, Machine Learning, Mechanical Properties
Published online 5/7/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Chukwuemeka Okolo, Katharina Eissing, Richard Williams, Felix Jensch, Omar Fergani, Sebastian Härtel, Investigation of the influence of AI-controlled process parameter adjustment on the mechanical properties of LBPF-manufactured parts, Materials Research Proceedings, Vol. 54, pp 218-227, 2025
DOI: https://doi.org/10.21741/9781644903599-24
The article was published as article 24 of the book Material Forming
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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