Modeling the topographic properties of 3D printed metal materials
DOI:
https://doi.org/10.18690/analipazu.15.1-2.43-53.2025Keywords:
senzor additive manufacturing, selective laser melting, surface roughness, fractal geometry, network theory, genetic programmingAbstract
This research presents a groundbreaking method that combines fractals, network theory, and genetic programming to analyze the surface topography of metal additive parts. Specifically, it focuses on the selective laser melting of EOS MS1 maraging steel processed with the EOS M 290 3D printer. The findings show a significant improvement in the accuracy of surface roughness characterization using genetic programming. The use of fractal geometry and machine learning has improved our understanding of the complexity of selective laser melting of surfaces. This study not only contributes to the field of additive manufacturing by offering a more efficient and accurate approach to quality control, but also lays the foundation for future research into other materials and the refinement of analytical techniques. The potential of this method in supporting metal 3D printing practices is considerable, indicating a promising future for the industry in terms of both innovation and application.
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References
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