additive manufacturing engineers

Abstract: Discrete multi-group pipe routers place channels on a grid or coarse graph. The resulting centerlines are topologically feasible but typically contain many axis-aligned corners. For permanently occupied fluid tubes---as in additively manufactured hydraulic manifolds---those corners are undesirable: they degrade geometric fairness and, in practice, flow quality. This technical report studies the downstream geometric stage that starts from a fixed routing topology and a feasible coarse layout, and refines tubular centerlines under permanent clearance. Unlike classical robotic path smoothing, which usually fair one temporary open trajectory, our setting is a branched manifold with declared terminal usages, finite tube radii, and static cross-group occupancy. We formalize the refinement problem, highlight the multi-usage junction conflict that a single shared branch point cannot resolve, and outline a hybrid trunk-share / junction-split parameterization with soft geometric costs and hard clearance acceptance. On a multi-group suite ($8$ groups $\times$ $8$ terminals, $r{=}2$) with capsule clearance, the refine reaches full hard-accept success, $κr\lesssim 1$, and about a $95\%$ relative drop in the soft objective. Physics-based pressure-drop optimization is intentionally deferred to a later PDE stage; here the objective remains geometric.

Abstract: Lattice metamaterials support lightweight, multifunctional structures, while additive manufacturing (AM) enables complex geometries. Yet multiphysics lattice design faces two challenges: efficiently constructing well-covered multi-objective Pareto fronts under limited budgets, and satisfying manufacturing constraints such as overhangs, enclosed cavities, and restricted powder-removal channels. We propose a manufacturing-constraint-driven method for lattice optimization and Pareto-front construction. Differentiable manufacturing constraints are embedded in inverse-homogenization topology optimization, enabling joint optimization of physical performance and manufacturability. A progressive Pareto-front mechanism uses a density-generation network to learn latent representations of high-quality lattices, interpolates neighboring nondominated representations, and decodes them into initial density fields for subsequent optimization. Newly found nondominated solutions update the network and sample set, progressively expanding the manufacturable set. On 3D periodic unit cells, with 1000 optimization runs, network initialization achieves a 92.60% success rate and 916 manufacturable samples, versus 78.30% and 776 for random initialization. Its Pareto front reaches a hypervolume of 0.0787, compared with 0.0675 for random initialization. The results show that the method efficiently constructs broadly covered manufacturable Pareto fronts for multiple physical properties.

Abstract: Reliable prediction of crystallographic texture in laser powder bed fusion is critical for linking process conditions with anisotropic response and for qualification. However, black-box models may fail under shift and cannot distinguish weak data support from loss of physical validity. This study develops a two-stage physics-based model for <001> || BD (build direction) texture in Inconel 718. Stage 1 maps process variables to melting mode and melt pool geometry. Stage 2 predicts texture by combining an empirical physics model with a random-forest residual model. A k-nearest-neighbor weight attenuates residual corrections for poorly supported queries, while a study-specific areal beam-power-density criterion withholds predictions outside the adopted conduction envelope. Conformal intervals are evaluated on the retained physics-valid set, and SHAP and Sobol analyses assess residual sensitivity. Under a controlled leave-one-defocus-out evaluation, the physics anchor achieved R^2 = 0.778, against -0.001 for the black-box model and 0.750 for the gated hybrid. Under leave-one-group-out cross-validation, the gated hybrid reached R^2 = 0.592 against 0.538 for the black-box model. Retained-set coverage was 92.9% at a mean full width of 3.65 multiples of a uniform distribution (MUD) under grouped cross-validation and 100% at a width of 3.21 MUD under transfer to a withheld +80 mm defocus regime. An illustrative mapping produced a retained BD elastic-modulus span of 127-187 GPa. On nine conditions from a separately built sample set, the framework withheld three, attenuated three, and matched the measured ordering for the rest. Separating data applicability, physics validity, and predictive uncertainty into distinct decisions lets the framework transfer where an unconstrained model does not, and withhold predictions where no model class performs adequately.

Abstract: The reuse of metal powders in directed energy deposition using laser powder offers promising sustainability benefits for additive manufacturing, yet its impact on part quality remains unknown. This study investigates the influence of powder reuse on the directed energy deposition process of Inconel 625. Virgin powder was first characterized in terms of flowability, morphology, size and chemical composition, then used to manufacture parts under fixed process parameters. These parts were analysed through two configurations (as-built and hot isostatically pressed) to assess microstructural evolution and mechanical performance. Results show that hot isostatic pressing reduces porosity and promotes grain recrystallization, enhancing ductility while decreasing tensile strength. Recovered powders from one manufacturing cycle, were also characterized. Particle size distribution and morphology remained stable, interstitial contents, particularly oxygen, increased, potentially affecting laser-material interaction and melt pool behaviour in future production. These findings highlight the importance of evaluating powder reuse through a full-process approach, from powder to final part. Future work will focus on the tensile and fatigue performance of parts produced with recovered powders.