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.

Finite element modeling for designing and optimizing lightweight titanium aerospace components requires advanced simulation tools with adequate material modeling. In this sense, a hybrid strategy is proposed in this work to identify the parameters of the Johnson–Cook plasticity and damage laws using a combined direct-inverse method. A direct calibration method for plasticity law is applied based on the literature-reported data of strain-stress curves from experimental tensile tests at different temperatures and strain rates. The triaxiliaty-dependent fracture parameters of the Johnson–Cook damage law at reference conditions of strain rate and temperature (d₁, d₂, and d₃) are calibrated with the direct method based on new data of experimental evolution of computed average fracture strain with the average stress triaxiality. The validation is performed with numerical results from an accurate micromechanics-based Ti64 model. The inverse calibration method is used to determine the strain rate and temperature-dependent damage parameters (d₄ and d₅) through large strain simulations of uniaxial tensile tests. The numerical results, including average strain and necking profile at fracture, are then utilized to calculate stress triaxiality by the Bridgman criterion for adjusting parameters d₄ and d₅. The calibrated model yields a 2.1% error for plasticity and 3.4% for fracture predictions. The experimental and simulated load-bearing capacity using the micromechanics damage model differed by only 1%. This demonstrates that the SC11–TNT model of Ti64 is reliable for identifying the Johnson–Cook damage law through the accurate use of inverse methods. The hybrid calibration strategy demonstrates the potential capability of the identified Johnson–Cook model to accurately predict the design load-carrying capacity of Ti64 aerospace components under different deformation rates and temperatures while accounting for material damage effects.

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.

Achieving the full potential of wind energy in the global renewable transition depends critically on enhancing the performance and reliability of polymer composite components. This review synthesizes recent advances from 2022 to 2025, including the development of next-generation hybrid composites and the application of high-fidelity computational methods—finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI)—to optimize structural integrity and aerodynamic performance. It also explores the transformative role of artificial intelligence (AI) in structural health monitoring (SHM) and the integration of Internet of Things (IoT) systems, which are becoming essential for predictive maintenance and lifecycle management. Special focus is given to harsh offshore environments, where polymer composites must withstand extreme wind and wave conditions. This review further addresses the growing importance of circular economy strategies for managing end-of-life composite blades. While innovations such as the geometric redesign of floating platforms and the aerodynamic refinement of blade components have yielded substantial gains—achieving up to a 30% mass reduction in PLA prototypes—more conservative optimizations of internal geometry configurations in GFRP blades provide only around 7% mass reduction. Nevertheless, persistent challenges related to polymer composite degradation and fatigue under severe weather conditions are driving the adoption of real-time hybrid predictive models. A bibliometric analysis of over 1000 publications confirms more than 25 percent annual growth in research across these interconnected areas. This review serves as a comprehensive reference for engineers and researchers, identifying three strategic frontiers that will shape the future of wind turbine blade technology: advanced composite materials, integrated computational modeling, and scalable recycling solutions.