Abstract

PurposeRelative density (RD) is a key quality indicator in laser-based powder bed fusion (L-PBF), linked to microstructure, mechanical properties and performance. This study aims to improve the prediction of RD by integrating a wider set of continuous and categorical inputs, capturing multifactorial interactions beyond the process parameters.Design/methodology/approachA data set of 1,579 samples was compiled from 85 peer-reviewed studies, covering multiple alloys, atmospheres, geometries and measurement methods. Exploratory data analysis combined mutual information and correlation metrics to assess feature relevance. K-means clustering segmented the data into homogeneous groups. Within each cluster, ensemble learning models were optimized via grid search and metaheuristics, with performance validated against literature and experimental data.FindingsThe cluster-driven framework achieved high predictive accuracy (R2= 0.94) across alloys and process ranges. Clustering improved generalization, especially in low-density regimes. Feature relevance varied by cluster: powder D50, geometric factor and laser power consistently ranked highest. Gradient boosting performed best in some clusters, while weighted-sum and voting ensembles provided the most balanced accuracy. SHAP analysis revealed complex, nonlinear interactions among geometric and process parameters.Originality/valueThis work introduces several novel contributions to the prediction of RD in L-PBF: the expansion of the input feature space to include underused variables such as material, shielding atmosphere, geometric descriptors and a newly defined shape factor; the use of a cluster-specific modeling strategy ("cluster-then-model") that tailors regressors to data subgroups based on process-response similarity; and the integration of dual-ensemble optimization with explainability methods, resulting in a robust, transferable and interpretable framework for process performance prediction in metal additive manufacturing.