Additive manufacturing enables the fabrication of lightweight structures with complex geometries, offering significant potential in aerospace and biomedical applications. Triply periodic minimal surface (TPMS) lattice structures are of particular interest due to their geometry. However, their intricate geometries pose challenges for both experimental characterization and numerical simulation. This study numerically investigates the effective mechanical properties and dynamic response of AlSi10Mg TPMS structures produced by laser powder bed fusion (PBF-LB/M). Using a micro–mesoscale approach with periodic boundary conditions, Young’s modulus, shear modulus, and Poisson’s ratio in the elastic region using the Johnson–Cook plasticity model are analyzed. Finite element simulations of the representative volume element (RVE) are employed to assess energy absorption and damage evolution under high strain rates, incorporating a ductile damage model. The performance of sheet-based TPMS lattices, namely, Schwarz–Primitive, Gyroid, Schwarz–Diamond, and IWP, is compared with strut-based lattices, namely, BCC and FCC, across volume fractions of 20–40%. Results demonstrate the superior stiffness and energy absorption of TPMS lattices, where Schwarz–Diamond and IWP outperformed the other structures, highlighting their advantages over conventional strut-based designs. This comprehensive numerical framework provides new insights into the high strain-rate behavior of TPMS structures and supports their design for demanding engineering applications.

Purpose: The purpose of this paper is to present the implementation of a physics-informed neural network (PINN) to predict the relative density of copper following the laser powder bed fusion process (LPBF). Design/methodology/approach: A 532 nm laser system is used to manufacture specimens from pure copper powder. The process parameters are varied to reduce the number of pores in the specimens. A specified set of the most relevant process parameters is used. Data from these experiments and literature data are combined to train the PINN. A control data set is used to compare the predictions of the PINN to experimentally determined data, as well as the predictions of other methods, like linear regression and a conventional artificial neural network. Findings: PINN are capable to predict the relative density of parts based on a limited amount of data. For predictions of the relative density for copper samples manufactured on the 532 nm LPBF system, the RMSE is 2.19 percentage points for an artificial neural network, 1.80 percentage points for a linear regression and 1.46 for the implemented PINN. Similar improvements in the quality of predictions can be found for predictions based on data extracted from published research. Research limitations/implications: The research conducted for this paper is limited to the LPBF process, but the implications suggest applicability to a much broader range of technologies which are limited by the number of possible experiments and are too complex to model or simulate otherwise. Practical implications: PINNs pose the opportunity to analyze systems which could otherwise not be analyzed properly because of a lack of data. Therefore, the presented PINN approach can facilitate the development of a variety of other processes with similar problems, while reducing the number of experiments necessary to generate training data. Originality/value: To the authors knowledge, no PINN has been applied for the prediction of process outcomes in LPBF based on process parameters, especially in the field of the additive manufacturing of pure copper.

Triply periodic minimal surface (TPMS) lattices offer strong potential for lightweight structural and energy-absorbing applications, however, the coupled influence of lattice topology, relative density, and post-processing-induced microstructural changes on their mechanical response remains insufficiently understood. In this study, Gyroid, Diamond, Primitive, and IWP AlSi10Mg TPMS lattices were fabricated via laser powder bed fusion (PBF-LB/M) at three relative densities and investigated in both as-built and EOS T6 heat-treated conditions. Quasi-static compression testing up to 50% strain was performed to evaluate stiffness, strength, deformation behavior, and energy absorption. While increasing relative density consistently enhanced stiffness, strength, and specific energy absorption, the magnitude and scaling of these improvements were found to depend strongly on topology and material condition. Heat treatment reduced yield strength and peak stress, but also altered deformation behavior and energy absorption characteristics in interaction with the other design variables. The results reveal that the compressive performance of TPMS lattices cannot be accurately described through independent effects of topology, density, or heat treatment alone, but instead emerges from their coupled interactions. This study therefore provides new insight into the structure–property relationships governing additively manufactured TPMS lattices and offers design considerations within the tested parameter space for tailoring mechanical performance through the coupled control of architecture and material condition.