Relative 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.
A 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.
The cluster-driven framework achieved high predictive accuracy (= 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.
This 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.
