20.500.12556/DKUM-97573 Prediction of bulk density in laser powder bed fusion of pure zinc using supervised machine learning This work used machine learning to forecast product density and optimize the laser powder bed fusion (LPBF) process for parts made of pure zinc (Zn). A relative density of 90–97% (6.42–6.95 g/cm3) was obtained by varying combinations of key process parameters, including laser power, scanning speed, track overlapping, hatch spacing, and layer thickness. Machine learning provided models for density prediction and better comprehension of the impact of input parameters. A SHapley Additive exPlanation (SHAP) analysis quantified the contributions of specific features, enhancing model interpretability. Fifty-one experimental runs were used to test several methods, including Bayesian ridge, CatBoost, elastic net, lasso, linear regression, random forest, ridge regression, and XGBoost. CatBoost performed best, with a test coefficient of determination (R2) of 0.893, a mean absolute error (MAPE) of 0.010 and a root mean square error (RMSE) of 0.015. A feature importance analysis showed that laser power (49%) and scanning speed (42%) had the greatest influence, while hatch spacing (5%) and layer thickness (4%) had minimal impacts on product density. Therefore, selecting the correct optimized set of process parameters determines the resulting density and can support more efficient LPBF process development. additive manufacturing laser powder bed fusion zinc machine learning regression methods dodajalne tehnologije lasersko spajanje slojev praškastega materiala cink strojno učenje regresijske metode true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/2075-4701/16/3/309 Članek v reviji 2026-03-20 10:29:42 2026-03-20 10:29:43 2026-03-21 03:11:27 0000-00-00 00:00:00 2026 0 0 20 str. iss. 3, [article no.] 309 Vol. 16 March 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-02-12 2026-03-09 2026-03-11 681.5:669 271940099 10.3390/met16030309 2075-4701 metals-16-00309_(1).pdf metals-16-00309_(1).pdf 1 426C159CFC15FB8EA51A825630239911 0743989758c139d2cedaae19fa0e0df6a7dc0d4da06b5fd2b8fedd0822a55ccc 96335c17-243f-11f1-8d16-00155d0001a1 https://dk.um.si/Dokument.php?lang=slv&id=204683 https://www.mdpi.com/2075-4701/16/3/309 0 6df3b7f0-2444-11f1-8d16-00155d0001a1 https://dk.um.si/Dokument.php?lang=slv&id=204685 Fakulteta za strojništvo 0 0 0