Binder jetting is a promising additive manufacturing (AM) technique that enables complex geometries and efficient material utilization while avoiding the challenges of extreme thermal cycles and high cooling rates inherent to fusion-based AM. However, it typically suffers from residual porosity, stemming from the complex, multi-scale interactions governing powder behavior during spreading, binder deposition, and sintering. These multiphysics interactions call for predictive, well-calibrated models for each key step. Here, we aim to combine the Discrete Element Method (DEM) for powder spreading and Phase-Field (PF) modeling for sintering to build a comprehensive computational tool for predicting defect – particularly porosity – formation and evolution in binder jetting of steels. To enhance predictive reliability, we incorporate a Bayesian calibration framework to determine the parameter distributions governing the DEM and PF models. This probabilistic approach not only improves parameter estimation but also quantifies uncertainty in both the models and the calibration process. In this talk, we focus specifically on the Bayesian calibration and application of the DEM powder model. The DEM framework captures the behavior of individual powder particles using calibrated phenomenological parameters such as coefficients of friction and adhesion. Through an in-house, open-source Python library for Bayesian calibration, called ACBICI (A Configurable BayesIan Calibration and Inference package), we use reference data from classical powder characterization experiments (e.g., tapped density, angle of repose, and rolling cylinder) to calibrate these parameters. We propose an efficient sequential methodology for their identification. Ultimately, by applying similar calibration techniques to the sintering step, we aim to develop a fully integrated numerical tool equipped with advanced uncertainty quantification. This tool will provide a robust methodology for investigating defect formation in binder jetting and contribute to a deeper understanding of sintering dynamics – offering pathways to reduce residual porosity and produce high-performance, defect-free components.