Lattice structures emerged as a revolutionary class of materials with significant applications in aerospace, biomedical engineering, and mechanical design due to their exceptional strength-to-weight ratio, energy absorption properties, and structural efficiency. This review systematically examines recent advancements in lattice structures, with a focus on their classification, mechanical behavior, and optimization methodologies. Stress distribution, deformation capacity, energy absorption, and computational modeling challenges are critically analyzed, highlighting the impact of manufacturing defects on structural integrity. The review explores the latest progress in hybrid additive manufacturing, hierarchical lattice structures, modeling and simulation, and smart adaptive materials, emphasizing their potential for self-healing and real-time monitoring applications. Furthermore, key research gaps are identified, including the need for improved predictive computational models using artificial intelligence, scalable manufacturing techniques, and multi-functional lattice systems integrating thermal, acoustic, and impact resistance properties. Future directions emphasize cost-effective material development, sustainability considerations, and enhanced experimental validation across multiple length scales. This work provides a comprehensive foundation for future research aimed at optimizing biomimetic lattice structures for enhanced mechanical performance, scalability, and industrial applicability.

Inconel 718 (IN718) is a polycrystalline nickel-based superalloy and one of the most widely used materials in the aerospace industry owing to its excellent mechanical performances at high temperatures, including creep resistance. Interest in additively manufactured components in aerospace is greatly increasing due to their ability to reduce material consumption, to manufacture complex parts, and to produce out-of-equilibrium microstructures, which can be beneficial for mechanical behavior. IN718’s properties are, however, very sensitive to microstructural features, which strongly depend on the manufacturing process and subsequent heat treatments. Additive manufacturing and, more specifically, Laser Powder Bed Fusion (LPBF) induces very high thermal gradients and anisotropic features due to its inherently directional nature, which largely defines the microstructure of the alloy. Hence, defining appropriate manufacturing parameters and heat treatments is critical to obtain appropriate mechanical behavior. This review aims to present the main microstructural features of IN718 produced by LPBF, the creep mechanisms taking place, the optimal microstructure for creep strength, and the most efficient heat treatments to yield such an optimized microstructure.

This paper reports the sensitivity of the thermal and the displacement histories predicted by a finite element analysis to material properties and boundary conditions of a directed-energy deposition of a M4 high speed steel thin-wall part additively manufactured on a 42CrMo4 steel substrate. The model accuracy was assessed by comparing the simulation results with the experimental measurements such as evolving local temperatures and distortion of the substrate. The numerical results of thermal history were successfully correlated with the solidified microstructures measured by scanning electron microscope technique, explaining the non-uniform, cellular-type grains depending on the deposit layers. Laser power, thermal conductivity, and thermal capacity of deposit and substrate were considered in the sensitivity analysis in order to quantify the effect of their variations on the local thermal history, while Young’s modulus and yield stress variation effects were evaluated on the distortion response of the sample. The laser power showed the highest impact on the thermal history, then came the thermal capacity, then the conductivity. Considering distortion, variations of the Young’s modulus had a higher impact than the yield stress.

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson–Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model’s predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress–strain behavior of Ti64 alloy and three virtual materials.

Lattice structures produced by additive manufacturing are increasingly used in lightweight, load-bearing applications, yet their mechanical performance is strongly influenced by geometry, process parameters, and boundary conditions. This study investigates the compressive behavior of body-centered cubic (BCC) 316L stainless steel lattices fabricated by laser powder bed fusion (LPBF). Four relative densities (20%, 40%, 60%, and 80%) were achieved by varying the strut diameter, and specimens were built in both vertical and horizontal orientations. Quasi-static compression tests characterized the elastic modulus, yield strength, energy absorption, and mean force, while finite element simulations reproduced the deformation and hardening behavior. The experimental results showed a direct correlation between density and mechanical properties, with vertically built specimens performing slightly better due to reduced processing defects. Simulations quantified the effect of strut–joint rounding and the need for multi-cell configurations to closely match the experimental curves. Regardless of the boundary conditions, for a density of 20%, simulating a single cell underestimated stiffness because of unconstrained strut buckling. For higher densities and thicker struts, this sensitivity to boundary conditions strongly decreased, indicating the possibility of using a single cell for shorter simulations—a point rarely discussed in the literature. Both experiments and simulations confirmed Gibson–Ashby scaling for elastic modulus and yield strength, while the tangent modulus was highly sensitive to boundary conditions. The combined experimental and numerical results provide a framework for the reliable modeling and design of metallic lattices for energy absorption, biomedical, and lightweight structural applications.