This study proposes a hybrid wire arc directed energy deposition (WA-DED) process that uses 3D sand-printed supports to overcome the limitations of low-angle overhang fabrication. WA-DED is a metal additive manufacturing process offering high deposition rates, cost efficiency, and suitability for large-scale components. However, because of its high heat input and molten pool instability, low-angle overhang and hollow structures remain difficult to fabricate, as the molten metal tends to collapse under gravity and thereby degrade geometric accuracy and surface quality. To address this issue, sand-printed supports were introduced. The supports provide tailored mechanical constraint and guide the solidification of the molten pool during deposition. Experiments were conducted to evaluate the feasibility of the proposed process at various overhang angles. The results show that the hybrid process markedly improves deposition stability and enables the fabrication of low-angle and curved overhang structures that conventional WA-DED cannot produce. These findings confirm the effectiveness of sand-supported WA-DED and highlight its potential for industrial applications requiring complex geometries, such as aerospace, marine, and energy components.

Overhang structures are essential geometries in metal additive manufacturing for realizing complex shapes. However, achieving stable, support-free overhang structures requires precise control of process parameters, and securing shape fidelity becomes particularly challenging as overhang length increases due to thermal deformation. To address this challenge, this study proposed a Bayesian optimization framework for efficiently identifying optimal process parameters to fabricate high-difficulty overhang structures. An image-based scoring method was developed to quantitatively evaluate shape defects. Experimental data were collected by fabricating 3, 6, and 9 mm overhang structures with various process parameters. Based on collected data, Gaussian Process Regression (GPR) models were trained. A physics-informed soft penalty term based on energy density was incorporated to construct a surrogate model capable of making physically plausible predictions even in extrapolated regions. Using this model, Bayesian optimization was applied to overhang lengths of 12, 15, and 18 mm, for which no prior experimental data existed. Recommended parameters enabled stable, support-free fabrication of overhang structures. This study demonstrates that reliable optimization of process parameters for complex geometries can be achieved by combining minimal experimental data with physics-informed modeling, highlighting the framework’s potential extension to a wider range of geometries and processes