A generative design methodology is presented that solves for minimum volume and compliance space-frame systems, with consideration of stress and buckling constraints. The solution space is explored using formal topology optimisation routines. A parameterisation method converts voxelised topology optimisation solutions into skeletonised connectivity representations. An inequality constrained gradient descent optimisation method optimises and defines cross-sectional geometry. This enables fast and automatic solution generation, providing designers with sets of high-performing problem solutions. Skeleton representations provide an inexpensive modelling tool for parallel analysis of physical problems difficult to model using topology optimisation. Geometry is represented using traditional engineering cross-sections with well understood behaviour. This improves certainty in the performance of solutions, simplifying certification processes. The generative design of a structural aerospace bracket for additive manufacture has been used as a case study within this research. A design of experiments produced 360 topology optimisation results, altering input variables and discretisation resolution to identify their effect on solution outcomes and the performance of parameterisation. The proposed method was found to robustly generate high-performing solutions utilising low-resolution topology optimisation. Additionally, 6 high-performing topologies were identified, providing designers with a set of solutions to select from. Limitations on the parameterisation process were identified, with topology optimisation solutions with volume fractions above 0.2 not parameterising successfully, and simulations with a resolution of 5 mm element size and below generating low performing skeletonised topologies.

Heat-treated aluminium thin-walled tubes combine efficient energy absorptive properties with low weight, making them common engineering structures for impact mitigation. Recently, the integration of foam cores within these structures further increases their energy absorption capabilities, however, these foams are stochastic leading to unpredictable failure response and negligible load-bearing enhancements. This study instead integrates a heat-treated aluminium alloy (AlSi10Mg) strut-based lattice structure as the cores for thin-walled tubes through the additive manufacturing process of laser powder bed fusion. Fabricated for lattice relative densities between 10–20% the lattice reinforced thin-walled tubes observe a specific energy absorption increase by up to 223% from standard AlSi10Mg thin-walled tubes. Numerical modelling and experimental testing both confirm the excellent energy absorption of the proposed structures.

Surface roughness is traditionally evaluated with contact profilometry; however, these methods are not compatible with complex additive manufactured lattice structures due to limited physical access. For these scenarios, computed tomography (CT) is often used to provide qualitative insight into surface roughness but does not directly yield roughness profile data. This research describes a hybrid approach for the non-destructive quantification of roughness profile data for lattice structures based on the mathematical reconstruction and interpretation of CT data. Formal analyses are applied to propose the theoretical minimum CT voxel size required to characterise surface roughness for a specified sampling length. The method is verified against optical data for nominally flat metallic specimens and applied to metallic and polymeric cylinders fabricated by powder bed fusion and material extrusion respectively. This research also assesses the influence of CT reconstruction thresholding as a process variable and finds that roughness profile data is only weakly influenced by thresholding settings, due to scattering effects at the surface — a novel finding that provides certainty for the industrial application of this method. The ability of the proposed method to accurately characterise the inherent surface roughness of these processes as well as the effect of specimen orientation is thus demonstrated, enabling full geometric characterisation supporting subsequent certification analysis. The method can be algorithmically implemented in combination with the generative design of complex lattice structures to support structural certification requirements.

Additive manufacturing (AM) enables patient-specific lattice-based implants with porosity engineered to encourage bone ingrowth and to mimic bone’s mechanical stiffness. The strength of the bone-implant interface can be measured through a destructive ‘push-out’ testing. The aim of this study is to explore the effect of implant-bone stiffness ratio (γ) on the push-out force using numerical simulation and a small experimental study. Numerical simulations of an implant-bone interface during a push-out test showed a fundamental change of failure mode for γ ranging from 0.1 to 10. For the geometry considered, the largest push-out forces were predicted for γ ≈ 0.7, essentially doubling the push-out force compared to a solid titanium implant. The experimental and simulation results also demonstrated that using an intermediate stiffness metal implant lattice geometry, γ ≈ 1.35, does not significantly improve the peak force of the push-out test compared to the solid implant. For the experimental study, critical-sized defects were simulated via robotic bone resection in the right lateral distal femur of a group of ~ 2.5-year-old healthy sheep, and then solid or lattice-based Ti6Al4V implants inserted. The femurs were harvested 6 months after implantation. Nine of the implanted femurs (six solid and three lattice-based) were used for fixation testing. The experimental study showed no significant difference in push-out force between a solid and moderately stiff lattice metal implant as indicated by the numerical simulation.