The metal additive manufacturing (AM) sector is limited by the scarcity of certified materials. Developing new materials is costly and time-consuming due to the complexity of AM processes where numerous parameters impact the part's quality.

The goal of this study is using simulation and empirical data of AlSi10Mg and an alloy from the Al5000 series (AlMgty80) to train ML models to predict part density in laser powder bed Fusion (PBF-LB/M). The models are used to predict process windows for a new alloy from the Al7000 series (AlZnty) to identify the potential of recognising process windows prior to empirical testing. By integrating simulated data, material influences are indirectly accounted for in the dataset. Various ML algorithms were trained and evaluated using root mean squared error (RMSE), R², and qualitative process map assessments.

Results show that ML models based on AlSi10Mg and AlMgty80 data accurately predict the density for the respective materials, achieving a RMSE around 1% and R² over 0.8. Predictions for AlZnty showed larger errors (RMSE ~3%, R² near zero), yet the qualitative evaluation of process maps show that high-density regions align with experimental data due to the model systematically underestimating the density.

Overall, the methodology demonstrates the ability to identify process windows for novel alloys using simulations and ML models based on similar alloys, despite limitations in density prediction accuracy. Future work will involve training models including AlZnty data to iteratively enhance accuracy. Furthermore, limited data availability continues to challenge ML in AM, highlighting the need for extensive databases.