abstract = "The present study introduces a novel approach to
analyse the surface roughness of metal parts made by 3D
selective laser melting (SLM). This technology, known
for its ability to efficiently produce functional
prototypes and limited-run series, is particularly
effective when surface conditions directly meet usage
requirements. Thus, the suitability of surfaces is a
critical factor, emphasising the importance of new
methods for predicting their quality. Here fractal
geometry and network theory are integrated to delve
into the complexities of SLM-produced surfaces, while
machine learning and pattern recognition concepts are
employed to evaluate the surface roughness.
Specifically, genetic programming, artificial neural
networks, support vector machine, random forest,
k-nearest neighbors are compared in terms of accuracy
demonstrating that only the first method provided valid
estimation due to the presence of very little training
data. Experimental work with EOS Maraging Steel MS1 and
an EOS M 290 3D printer validates the method's
practicality and effectiveness. Then, the research
offers a fresh perspective in surface analysis and has
significant implications for quality control in
additive manufacturing, potentially enhancing the
precision and efficiency of 3D metal printing.",