Process-parameter recommendations in material extrusion are often derived at one fixed infill geometry and then transferred to others, although such studies cannot estimate topology × process interactions. This study tests the transferability of process-parameter recommendations for lightweight polylactic acid (LW-PLA) using the phase 2 experimental campaign reported previously. A 3 × 2 × 2 factorial experiment (Cubic, Gyroid, Sub-Cubic; 230/240 °C; 70/80% flow) was reanalyzed by Type III sum-coded analysis of variance (ANOVA) with effect-size decomposition for ultimate tensile strength (UTS; N = 68), apparent density ( N = 59), and Charpy impact energy ( N = 71). For UTS, the Topology × Flow is the largest term [ η 2 = 33.6%; partial η 2 = 71.3%; F (2,56) = 69.53, p < 0.001], exceeding the flow main effect ( η 2 = 26.5%). Raising flow from 70% to 80% changes UTS by +1.020 MPa for Cubic, +0.703 MPa for Sub-Cubic, and −0.284 MPa for Gyroid; all pairwise interaction contrasts survive Holm correction. Density increases for every topology and most for Gyroid (+14.4%), despite its strength loss. Accordingly, marginal mass efficiency of the flow step is +68.0 MPa cm 3 /g for Cubic, +29.3 for Sub-Cubic, and −9.6 for Gyroid, inconsistent with a simple gravimetric explanation. In Charpy impact, topology is the only detectable systematic term [ η 2 = 41.7%, F (2,59) = 25.36, p < 0.001], with no detectable process main effect or interaction. Within the investigated 70–80% step, the tensile response therefore depends on topology; process recommendations should not be assumed to be transferable across architectures without interaction testing.
This study advances the structural understanding of Hollow-Core 3D-printed elements, a robotic large-format additive manufacturing method, and evaluates their applicability for building and facade components through a combined experimental and numerical investigation. Focusing on single-pane and double-pane panels, the research examines how nozzle design and fabrication parameters govern structural behavior. For single-pane Hollow-Core 3D Printing elements, analytical calculations of bead cross-sections were combined with finite element analysis of panels to identify structurally efficient nozzle profiles and printing strategies. Selected nozzle geometries were fabricated and tested under three-point bending to quantify the influence of bead wall thickness, layer height, and nozzle section design on flexural performance. For double-pane typologies incorporating triangulated infills, four-point bending tests were conducted on 12 specimen groups to evaluate the effects of panel thickness, print orientation, and infill angle. Impact performance of single-pane panels was further preliminarily assessed through standardized soft-body and hard-body pendulum tests. The results demonstrate that print orientation relative to supports is a first-order design parameter, inducing pronounced structural anisotropy. Increasing bead wall thickness enhances load capacity by 3-5.5 times. Circular-crown nozzle sections achieve the highest stiffness and strength, while noncircular profiles (e.g., 2X24W1.5) provide comparable structural performance with improved material efficiency and reduced thermal conductivity. In double-pane systems, stiffness and span capacity are influenced primarily by the print direction, followed by the panel depth, and then the infill angle. Preliminary impact tests indicate energy dissipation of 60-70% under soft-body impacts and 30-45% under hard-body impacts at the center of the panel.
Recent advances in machine learning (ML) and additive manufacturing have created new opportunities for generating multifunctional engineering designs. One emerging application is the integration of ML-driven generative design with bioinspired concepts for 3D concrete printing (3DCP). By learning geometric features and design principles from biological systems, ML models can generate novel design candidates that satisfy predefined performance objectives and manufacturing constraints. A conceptual framework is proposed that integrates biological inspiration, dataset preparation, generative modeling, design evaluation, and fabrication through 3DCP. Key challenge for implementation includes the printability constraints associated with overhanging geometries. This short communication discusses the potential of combining ML-based generative design and 3DCP to develop bioinspired concrete structures and outlines future research directions for applications in extreme environments.
This study investigates the compressive energy absorption behavior of parametrically defined, strut-based mesostructures fabricated using stereolithography additive manufacturing. Lattice specimens with four distinct geometries-body-centered cubic, octet, re-entrant, and Kelvin cell-were designed using implicit modeling, maintaining constant relative density and unit cell dimensions to enable direct comparison of structural efficiency. Specimens were printed with Formlabs Clear resin, chosen for its superior energy absorption and elastic properties, and subjected to uniaxial compression testing using a Tinius Olsen universal testing machine. Experimental protocols ensured uniform loading and minimized size-dependent effects. Force-displacement data were processed to quantify energy absorption and benchmarked against prior literature and other mesostructure types using Ashby plots and bar chart visualizations. Results reveal that all strut-based lattices exhibited predominantly brittle behavior under compressive loading, characterized by abrupt failure and limited plastic deformation. Compared with serpentine and plate-based mesostructures, which show more ductile and progressive energy dissipation, the tested strut-based designs absorbed less energy and failed at higher densities. This study highlights the critical influence of lattice topology on mechanical response, demonstrating that slender strut-based architectures concentrate stress and are prone to catastrophic fracture, whereas alternative geometries, such as serpentine and plate-based lattices, facilitate distributed deformation and superior energy absorption. These findings underscore the importance of geometric innovation in the design of architected materials for impact mitigation and energy-absorbing applications. The research identifies current limitations associated with strut-based periodic lattices and points to future directions involving plate-based, hybrid, and multiscale lattice architectures. Integrating computational modeling with experimental validation is recommended for optimizing both geometry and material properties, ultimately advancing the functional performance of energy-absorbing mesostructures.
Friction stir additive manufacturing (FSAM) is a promising solid-state route for aluminum–lithium (Al–Li) alloy structural fabrication, yet interfacial defects and microstructural heterogeneity remain key challenges. This study investigates the effects of rotational speed, traverse speed, and overlap ratio on defect formation and microstructural evolution in 2060-T3 Al–Li alloy, with postdeposition heat treatment further evaluated for property optimization. At 800 r/min and 300 mm/min, a void-free cross-section is achieved with a nugget zone grain size of 1.45 μm, a dynamic recrystallization fraction of 67.64%, and a high-angle grain boundary (HAGB) fraction of 92.29%; suboptimal speeds produce either internal voids or surface depression and flash. An 80% overlap ratio eliminates hook defects via secondary recrystallization, achieving an effective build width of 7.14 mm versus 5.05 mm at 90% overlap. Solution treatment at 525°C/0.5 h followed by single-stage aging at 145°C/35 h promotes uniform Al 2 Cu reprecipitation, delivering a peak microhardness of 170.6 HV0.2 and a stable HAGB fraction of 82.1%. These findings establish a three-stage defect control strategy—intrapass optimization, interpass overlap management, and postdeposition heat treatment—providing a systematic pathway for high-performance FSAM of Al–Li alloy structural components.
