| Abstract Scope |
Wetting behavior plays a central role in melt pool stability, interlayer bonding, and defect formation in laser powder bed fusion (LPBF). Despite its importance, predictive models linking melt pool geometry to contact angle remain largely empirical and system-specific, limiting their transferability across materials and process conditions. In this work, we propose a general, physics-informed scaling-law framework for predicting contact angle based on melt pool geometric characteristics.
A set of geometric descriptors include dimensionless ratios is constructed from measurable melt pool features, including depth, width, height, layer thickness, and cross-sectional areas. These descriptors capture aspect ratios, area fractions, compactness, and normalized geometric measures while avoiding explicit dependence on absolute length scales. The contact angle is modeled using power-law scaling relationships of the form , which are linearized in logarithmic space and fitted using L2-regularized regression to ensure robustness in the presence of correlated features.
An exhaustive evaluation of feature combinations is performed to identify scaling laws that balance predictive accuracy and physical interpretability. Model performance is quantified using multiple statistical metrics, including coefficient of determination and error-based measures. The resulting models demonstrate strong predictive capability, with consistent trends observed across different geometric descriptors. The fitted scaling exponents reveal key geometric factors governing wetting behavior.
This scaling-law framework provides a general and interpretable approach for connecting melt pool geometry to wetting behavior in LPBF. By relying on dimensionless descriptors, the proposed methodology offers a pathway toward transferable models that can support process understanding, parameter optimization, and data-driven process design in metal additive manufacturing. |