3129 KB0 KB199920252024Babič, MatejFragassa, CristianoKovačič, MihaŠturm, Romanštevilka:7/8letnik:70str. 313-324ISSN:0039-2480DOI:10.5545/sv-jme.2024.1009COBISSID_HOST:209426179URN:URN:NBN:SI:doc-WZOIOD3KenZveza strojnih inženirjev in tehnikov Slovenije etc.Strojniški vestnikadditive manufacturingaditivna proizvodnjafractal geometryfraktalna geometrijagenetic programminggenetsko programiranjehrapavost površinenetwork theoryselective laser meltingselektivno lasersko taljenjesurface roughnessteorija omrežijSelective laser melting| a novel method for surface roughness analysis|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 requir ements. Thus, the suitability of surfaces is a critical factor, emphasizing 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 printingTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za strojništvo