Finite Element Method Simulation Research Articles

Experimental and numerical investigations to the aeroelastic response of flexible thin airfoil

The paper investigates the phenomenon of the aeroelastic response of flexible thin airfoils under various angles of attack (AOAs) and flow velocities through wind tunnel experiments and numerical simulations. The vibration modal characteristics are explored, including vibration frequencies, amplitudes, modal transition, and instantaneous characteristics. Vibration is directly measured using non-contact laser sensors, and the numerical model is appropriately configured to simulate the fluid–structure interaction (FSI) problem under large deformation. Experiments cover a range of AOAs (1°–20°) and incoming velocities (from 10 to 73 m/s), with dynamic responses measured using four laser sensors. Both average and instantaneous modal response features are analyzed, revealing multi-modal characteristics as velocity and AOA increase. The vibration mode transitions from pure bending to higher-order modes as incoming velocity increases. Specifically, at higher velocities and increased AOA, the high-order vibration component shifts from bending-torsional coupled mode to pure-torsional mode. Comparison of vibration frequencies between experimental measurements and finite element method simulations highlights significant shifts, particularly in the pure-torsional mode. Furthermore, employing commercial software ANSYS CFX and ANSYS Mechanical, a two-way three-dimensional FSI model successfully replicates flutter boundaries observed experimentally at 1° AOA and approximately incoming velocity 73 m/s. This FSI model is extended to simulate the multi-modal vibrations at 15° AOA, yielding insights into flow phenomena contributing to multi-modal vibration at this AOA. An explanation is provided for the multi-modal vibration phenomenon observed in the experiments based on the above insight. Finally, the differences between the experimental and numerical simulations are speculated upon.

Solder Defect Assessment Method for Power Electronic Devices Based on Mechanical Stress Wave Signals

The health of a power electronic device is closely related to the mechanical stress waves released at the moment when the device is turned on and off. Research has shown the potential of mechanical stress waves in power electronic device health monitoring. However, the correlation between power device defect severity and mechanical stress wave signal characteristics has not been fully established and an effective evaluation method is currently lacking. This article uses finite element simulation methods to explore the impact of void defects in the chip solder and base plate solder of insulated gate bipolar transistor (IGBT) devices on mechanical stress wave signals. By analyzing the mechanical stress wave curves of cavity defects of multiple sizes, a method for assessing the health status of power devices based on principal component analysis (PCA) is proposed. By calculating the projection of the mechanical stress wave signal on the principal component, the characteristic quantity representing the severity of the defect is obtained. Simulation results indicate that for chip soldering defects, as the severity of defects increases, the feature values gradually decrease. Therefore, this method exhibits good evaluation performance for defects throughout the entire lifecycle. In the case of plate solder defects, this approach can effectively identify mid-to-late-stage defects in devices and can also be utilized for reliability prediction before device failure.

Analysing the shape memory behaviour of GnP-enhanced nanocomposites: a comparative study between experimental and finite element analysis

Shape memory polymers (SMPs) are capable of enduring significant deformations and returning to their original form upon activation by certain external stimuli. However, their restricted mechanical and thermal capabilities have limited their broader application in engineering fields. To address this, the integration of graphene nanoplatelets (GnPs) with SMPs has proven effective in enhancing their mechanical and thermal properties while maintaining inherent shape memory functions. The study evaluated shape memory nanocomposites (SMNCs) using dynamic mechanical, thermogravimetric, and static tensile, flexural, and shape memory tests, along with scanning electron microscopy to analyse tensile fractures. The results indicate that the optimal content of GnP is 0.6 wt%, resulting in excellent shape memory, thermal, and mechanical properties. Specifically, this composition demonstrates a shape recovery ratio of 94.02%, a storage modulus of 4580.07 MPa, a tensile strength of 61.42 MPa, and a flexural strength of 116.37 MPa. Additionally, the incorporation of GnPs into epoxy reduces recovery times by up to 52% at the 0.6 wt% concentration. While there is a slight decrease in the shape fixity ratio from 98.77% to 93.02%, the shape recoverability remains consistently high across all samples. Current finite element (FE) models often necessitate complex, problem-specific user subroutines, which can impede the straightforward application of research findings in real-world settings. To address this, the current study introduces an innovative finite element simulation method using the widely used ABAQUS software to model the thermomechanical behaviour of SMNCs, importantly incorporating the time-dependent viscoelastic behaviour of the material. The effectiveness of this new approach was tested by comparing experimental results from bending test of SMNCs cantilever beam with outcomes derived from FE simulations. The strong agreement between the experimental data and simulation results confirmed the precision and reliability of this novel technique.

Design and analysis of mode-matched decoupled mass MEMS gyroscope with improved thermal stability

This paper presents an efficient design approach for microelectromechanical systems (MEMS) gyroscope considering the design constraint of MEMSCAP’s Silicon-on-Insulator Multi-User MEMS Processes (SOIMUMPs). A design for a decoupled mass MEMS resonant gyroscope with tuning electrodes is presented to minimize cross-axis coupling and optimize the gyroscope’s performance. The device features a size of 1.8 ×2.772 mm2 . A decoupled mass MEMS gyroscope has been designed using a novel mechanical beam configuration that optimizes the structural design to reduce unwanted interactions between drive and sense axes motions. Mode order has been achieved by obtaining the first two modes as drive and sense modes at 9946.3 Hz and 10 202.7 Hz, respectively. Parallel plate electrostatic tuning electrodes have been added to adjust and match the drive and sense modes at the resonant frequencies of 9946 Hz. This mode matching is crucial for optimizing gyroscope performance, accuracy, and sensitivity. The effects of temperature variation on the performance of the designed decoupled mass MEMS gyroscope have been investigated, particularly in terms of its mode-matched operation achieved using tuning electrodes. Finite element method (FEM) simulations were conducted, demonstrating that thermal stability was achieved, and mode-matched operation was maintained within the temperature range of −40 °C to 80 °C. The gyroscope exhibits enhanced performance compared to existing MEMS gyroscopes under mode-matched conditions, featuring a lower temperature coefficient of frequency (TCF) at 0.0415 Hz °C−1 in the drive mode and 0.0165 Hz °C−1 in the sense mode. FEM analysis for the fabrication process tolerances has been done, where MEMS gyroscope’s resonant frequencies, output capacitance, output displacement, and sense mode quality factor have shown negligible changes to fabrication process tolerances. Transient analysis was conducted using CoventorWare MEMS+ 3D schematic, which was integrated with MATLAB Simulink to measure the MEMS gyroscope’s output response, which corresponded with the varying input angular velocity signal.

Effect of Thermomechanical Processing on the Transformation Kinetics, Microstructure and Mechanical Properties of a Continuously Cooled Cementite-free Bainitic Steel

Abstract The combination of forming with continuous cooling bainitic steels offers a new path for reaching energy-efficient manufacturing chains. Improved energy efficiency is achievable thanks to the suppression of conventional, energy-intensive heat treatments. In the present paper, different thermomechanical processing strategies, including laboratory and industrial scale forgings, were assessed alongside their impact on the resulting microstructure. Firstly, phase transformation kinetics were assessed in precisely controlled environments via dilatometry coupled to in situ techniques. Secondly, the microstructure, mechanical properties, and forgeability of large-scale forged components were investigated in laboratory and industrial conditions. These investigations were also assisted by finite element method simulation. The comparison between controlled and industrial-scale conditions illustrates pitfalls in the transfer of knowledge to conditions approaching a real manufacturing chain. Nevertheless, alloy and process design is shown to be a key aspect to overcome the discussed challenges, allowing homogeneous bainite microstructures and mechanical properties to be achievable over a flexible range of processing conditions.

Pillar electrodes embedded in the skeletal muscle tissue for selective stimulation of biohybrid actuators with increased contractile distance.

Electrodes are crucial for controlling the movements of biohybrid robots, but their external placement outside muscle tissue often leads to inefficient and non-selective stimulation of nearby biohybrid actuators. To address this, we propose embedding pillar electrodes within the skeletal muscle tissue, resulting in enhanced contraction of the target muscle without affecting the neighbor tissue with a 4 mm distance. We use finite element method simulations to establish a selectivity model, correlating the VIE(volume integration of electric field intensity within muscle tissue) with actual contractile distances under different amplitudes of electrical pulses. The simulated selective index closely aligns with experimental results, showing the potential of pillar electrodes for effective and selective biohybrid actuator stimulation. In experiments, we validated that the contractile distance and selectivity achieved with these pillar electrodes exceed conventional Au rod electrodes. This innovation has promising implications for building biohybrid robots with densely arranged muscle tissue, ultimately achieving more human-like movements. Additionally, our selectivity model offers valuable predictive tools for assessing electrical stimulation effects with different electrode designs.

Penetration Characteristics of Mono-Column Composite Bucket Foundation for Offshore Wind Turbines

To address the issue of seepage and soil plugging during the sinking of mono-column composite bucket foundations (MCCBFs), experiments were conducted on the installation of foundations under a variety of complex geological conditions. The effect of negative pressure sinking mode on the foundation’s seepage field was analyzed, the formation mechanism of soil plugging in sand was explained, and an efficient method for calculating the height of soil plugging was proposed in conjunction with these investigations. The findings demonstrate that the finite element method simulation of the soil’s seepage field is the most accurate, that the pumping volume of the foundation during sinking through is high, that the formation of the soil plug height is high, and that clear depressions can be seen on the foundation manifold’s exterior. The equation presented in this study makes it easier to forecast the height of a foundation plug and can offer direction for engineering design.

Artificial neural network-based sequential approximate optimization of metal sheet architecture and forming process

Abstract This paper introduces a sequential approximate optimization method that combines the finite element method (FEM), dynamic differential evolution (DDE), and artificial neural network (ANN) surrogate models. The developed method is applied to address two optimization problems. The first involves metamaterial design optimization for metal sheet architecture with binary design variables. The second pertains to optimizing process parameters in multi-stage metal forming, where the discrete nature arises owing to changing tool geometries across stages. This process is highly nonlinear, accumulating contact, geometric, and material nonlinear effects discretely through forming stages. The efficacy of the proposed optimization method, utilizing ANN surrogate models, is compared with traditionally used polynomial response surface (PRS) surrogate models, primarily based on low-order polynomials. Efficient learning of ANN surrogate models is facilitated through the FEM and Python integration framework. Initial data for surrogate model training is collected via Latin hypercube sampling and FEM simulations. DDE is employed for sequential approximate optimization, optimizing ANN or PRS surrogate models to determine optimal design variables. PRS surrogate models encounter challenges in dealing with nonlinear changes in sequential approximate optimization concerning discrete characteristics such as binary design variables and discrete nonlinear behavior found in multi-stage metal forming processes. Owing to the discrete nature, PRS surrogate models require more data and iterations for optimal design variables. In contrast, ANN surrogate models adeptly predict nonlinear behavior through the activation function's characteristics. In the optimization problem of Metal Sheet Architecture for design target C, the ANN surrogate model required an average of 4.6 times fewer iterations to satisfy stopping criteria compared to the PRS surrogate model. Furthermore, in the optimization of multi-stage deep drawing processes, the ANN surrogate model required an average of 6.1 times fewer iterations to satisfy stopping criteria compared to the PRS surrogate model. As a result, the sequential global optimization method utilizing ANN surrogate models achieves optimal design variables with fewer iterations than PRS surrogate models. Further confirmation of the method's efficiency is provided by comparing Pearson correlation coefficients and locus plots.

Finite Element Method Simulation and Experimental Investigation on the Temperature Control System with Groundwater Circulation in Bridge Deck Pavement

Finite Element Method Simulation and Experimental Investigation on the Temperature Control System with Groundwater Circulation in Bridge Deck Pavement

Loss-Optimized Design of Magnetic Devices.

Maximizing efficiency, power density, and reliability stands as paramount objectives in the advancement of power electronic systems. Notably, the dimensions and losses of magnetic components emerge as primary constraints hindering the miniaturization of such systems. Researchers have increasingly focused on the design of loss minimization and size optimization of magnetic devices. In this paper, with the objective of minimizing the loss of magnetic devices, an optimal design method for the winding structure of devices is proposed based on the coupling relationship between the loss prediction model and the design variables. The method examines the decoupling conditions between the design variables and the loss model, deriving optimized design closure equations for the design variables. This approach furnishes a technical foundation for the miniaturized design of miniature apparatuses incorporating magnetic components, offering a straightforward and adaptable design methodology. The finite element method simulation results and experimental measurement data verify the accuracy of the prediction of the proposed method and the validity of the optimal design theory of device loss.

Wind load resistance of prefabricated ALC-rock wool external wall panel system

Exterior wall enclosure systems in steel-frame residential buildings are prone to extensive cracking and detachment under wind loads. This study, through research and comparison of commonly used materials, identified the exterior wall panel system with the optimal comprehensive performance. To assess the wind load resistance of this system, 11 sets of tensile tests were conducted considering anchor bolt quantity and the bonding rate parameters between autoclaved lightweight concrete (ALC) panels and rock wool boards. The research investigated the failure modes, load-bearing capacity, and failure mechanisms of the external wall panel system under wind suction loads. Based on the experimental findings, a simplified finite element simulation method was proposed and subjected to parameter analysis. This analysis explored the impact of anchor bolt quantity and layout. The research indicates that under tensile loads, the external wall panel system primarily experiences interfacial damage between rock wool boards and coating mortars. Its load-bearing capacity consists of the net bonding force between rock wool boards and finishing layers, as well as the mechanical force between fiberglass mesh and anchors. The load bearing capacity can be enhanced by strengthening the overall integrity of the system and by expanding the contact area between the fiberglass mesh and the anchor plate, with specific structural measures such as increasing the number of anchor, the bond ratio, and the diameter of the anchor plate. The arrangement of anchors has a minor impact on the load-bearing capacity of the external wall panel system but significantly affects the deformation of rock wool boards. Based on experimental and finite element studies, a simplified calculation method for the tensile load-bearing capacity of external wall panels for engineering applications was further developed and proven to be accurate through comparison with experimental results.

High-Throughput Single-Entity Electrochemistry with Microelectrode Arrays.

We describe micro- and nanoelectrode array analysis with an automated version of the array microcell method (AMCM). Characterization of hundreds of electrodes, with diameters ranging from 100 nm to 2 μm, was carried out by using AMCM voltammetry and chronoamperometry. The influence of solvent evaporation on mass transport in the AMCM pipette and the resultant electrochemical response were investigated, with experimental results supported by finite element method simulations. We also describe the application of AMCM to high-throughput single-entity electrochemistry in measurements of stochastic nanoparticle impacts. Collision experiments recorded 3270 single-particle events from 671 electrodes. Data collection parameters were optimized to enable these experiments to be completed in a few hours, and the collision transient sizes were analyzed with a U-Net deep learning model. Elucidation of collision transient sizes by histograms from these experiments was enhanced due to the large sample size possible with AMCM.

A Data-Driven Approach for Cutting Force Prediction in FEM Machining Simulations Using Gradient Boosted Machines

Cutting simulations via the Finite Element Method (FEM) have recently gained more significance due to ever increasing computational performance and thus better resulting accuracy. However, these simulations are still time consuming and therefore cannot be deployed for an in situ evaluation of the machining processes in an industrial environment. This is due to the high non-linear nature of FEM simulations of machining processes, which require considerable computational resources. On the other hand, machine learning methods are known to capture complex non-linear behaviors. One of the most widely applied material models in cutting simulations is the Johnson–Cook material model, which has a great influence on the output of the cutting simulations and contributes to the non-linear behavior of the models, but its influence on cutting forces is sometimes difficult to assess beforehand. Therefore, this research aims to capture the highly non-linear behavior of the material model by using a dataset of multiple short-duration cutting simulations from Abaqus to learn the relationship of the Johnson–Cook material model parameters and the resulting cutting forces for a constant set of cutting conditions. The goal is to shorten the time to simulate cutting forces by encapsulating complex cutting conditions in dependence of material parameters in a single model. A total of five different models are trained and the performance is evaluated. The results show that Gradient Boosted Machines capture the influence of varying material model parameters the best and enable good predictions of cutting forces as well as deliver insights into the relevance of the material parameters for the cutting and thrust forces in orthogonal cutting.

Experiment-Validated multiphysics modeling, generalizable deep learning and interpretable global sensitivity analyses for thermoelectric generators

Despite extensive studies on thermoelectric materials in a thermoelectric generator (TEG), the inverse design of TEG operating conditions remains challenging. Recent efforts in deep learning of TEGs using artificial neural network (ANN) lack global sensitivity analyses and interpretation of the connection-weights, leading to unknown coupling between input parameters and errors in importance ranking. This work reports a deep learning framework for TEGs using generalizable ANN, interpretable local (LSA) and global sensitivity analyses (GSA). Sensitivity analyses are applied to determine how energy conversion efficiency (η) under the practical Neumann and Robin thermal boundary conditions is affected by TEG operating conditions, including hot-side heat flux (qh), cold-side convective heat transfer coefficient (hf), temperature of the cooling medium (Ta), load resistance (RL), electric contact resistance (Rc), and thermal contact resistance (Kc). First, multiphysics TEG simulation results based on 3D finite element method (FEM) are validated by our experimental measurements using a self-built TEG performance evaluation system. The partial derivative-based LSA is done on a 1D theoretical TEG model to obtain the trend in sensitivity factors. Then, an ANN model is trained and validated based on the FEM simulation results and applied to the GSA process, aiming at reducing the computational load. Variance-based GSA is applied on the ANN model to determine the importance ranking and reflect the coupling between input parameters. The variance-based GSA ranks the importance as qh>hf>RL>Ta≈Rc≈Kc, and reflects the coupling between qh and hf. Finally, to interpret the ANN and the hidden deep learning process, a method combining the connection-weights algorithm and neural interpretation diagram (NID) is proposed to extract the hidden information in the connection weights. The ANN model is proven generalizable towards larger ranges of operating conditions. This work provides a framework to design TEG operating conditions towards higher energy conversion efficiencies.

A novel design method based onmulti–objective optimization for graded lattice structure by additive manufacturing

PurposeWhile performance demands in the natural world are varied, graded lattice structures reveal distinctive mechanical properties with tremendous engineering application potential. For biomechanical functions where mechanical qualities are required from supporting under external loading and permeability is crucial which affects bone tissue engineering, the geometric design in lattice structure for bone scaffolds in loading-bearing applications is necessary. However, when tweaking structural traits, these two factors frequently clash. For graded lattice structures, this study aims to develop a design-optimization strategy to attain improved attributes across different domains.Design/methodology/approachTo handle diverse stress states, parametric modeling is used to produce strut-based lattice structures with spatially varied densities. The tailored initial gradients in lattice structure are subject to automatic property evaluation procedure that hinges on finite element method and computational fluid dynamics simulations. The geometric parameters of lattice structures with numerous objectives are then optimized using an iterative optimization process based on a non-dominated genetic algorithm.FindingsThe initial stress-based design of graded lattice structure with spatially variable densities is generated based on the stress conditions. The results from subsequent dual-objective optimization show a series of topologies with gradually improved trade-offs between mechanical properties and permeability.Originality/valueIn this study, a novel structural design-optimization methodology is proposed for mathematically optimizing strut-based graded lattice structures to achieve enhanced performance in multiple domains.

Nested structure role in the mechanical response of spicule inspired fibers

Euplectella aspergillum marine sponge spicules are renowned for their remarkable strength and toughness. These spicules exhibit a unique concentric layering structure, which contributes to their exceptional mechanical resistance. In this study, finite element method simulations were used to comprehensively investigate the effect of nested cylindrical structures on the mechanical properties of spicules. This investigation leveraged scanning electron microscopy images to guide the computational modeling of the microstructure and the results were validated by three-point bending tests of 3D-printed spicule-inspired structures. The numerical analyses showed that the nested structure of spicules induces stress and strain jumps on the layer interfaces, reducing the load on critical zones of the fiber and increasing its toughness. It was found that this effect shows a tapering enhancement as the number of layers increases, which combines with a threshold related to the 3D-printing manufacturability to suggest a compromise for optimal performance. A comprehensive evaluation of the mechanical properties of these fibers can assist in developing a new generation of bioinspired structures with practical real-world applications.

Ultrasound-Triggered Piezoelectric Catalysis of Zinc Oxide@Glucose Derived Carbon Spheres for the Treatment of MRSA Infected Osteomyelitis.

Currently, methicillin-resistant Staphylococcus aureus (MRSA)-induced osteomyelitis is a clinically life-threatening disease, however, long-term antibiotic treatment can lead to bacterial resistance, posing a huge challenge to treatment and public health. In this study, glucose-derived carbon spheres loaded with zinc oxide (ZnO@HTCS) are successfully constructed. This composite demonstrates the robust ability to generate reactive oxygen species (ROS) under ultrasound (US) irradiation, eradicating 99.788% ± 0.087% of MRSA within 15min and effectively treating MRSA-induced osteomyelitis infection. Piezoelectric force microscopy tests and finite element method simulations reveal that the ZnO@HTCS composite exhibits superior piezoelectric catalytic performance compared to pure ZnO, making it a unique piezoelectric sonosensitizer. Density functional theory calculations reveal that the formation of a Mott-Schottky heterojunction and an internal piezoelectric field within the interface accelerates the electron transfer and the separation of electron-hole pairs. Concurrently, surface vacancies of the composite enable the adsorption of a greater amount of oxygen, enhancing the piezoelectric catalytic effect and generating a substantial quantity of ROS. This work not only presents a promising approach for augmenting piezoelectric catalysis through construction of a Schottky heterojunction interface but also provides a novel, efficient therapeutic strategy for treating osteomyelitis.

Deep Learning‐Assisted Design of Bilayer Nanowire Gratings for High‐Performance MWIR Polarizers

AbstractOptical metamaterials have revolutionized imaging capabilities by manipulating light‐matter interactions at the nanoscale beyond the diffraction limit. Bilayer nanowire grating configurations exhibit significant potential as exceptional elements for high‐performance polarimetric imaging systems. However, conventional computational approaches for predicting electromagnetic responses are time‐consuming and labor‐intensive, and thereby, the practical implementation remains challenging through an iterative design, analysis, and fabrication process. Here, a deep learning‐based design process is presented utilizing an artificial neural network (ANN) trained on finite element method (FEM) simulations that enables the prediction of bilayer nanowire gratings‐based electromagnetic responses. The study validates predictions through nanoimprinted bilayer nanowire gratings, demonstrating the reliability of the ANN's predictions. Furthermore, the research identifies critical geometric parameters significantly influencing transverse magnetic (TM) and transverse electric (TE) transmission. The ANN model effectively tailors design for specific mid‐wavelength infrared (MWIR) wavelengths, which may provide a practical tool for rapidly designing and optimizing metamaterial for high‐performance polarizers.

Investigation of the Electrocatalytic Reduction of Peroxydisulfate Using Scanning Electrochemical Microscopy.

The elementary steps of the electrocatalytic reduction of S2O82- using the Ru(NH3)63+/2+ redox couple were investigated using scanning electrochemical microscopy (SECM) and steady-state voltammetry (SSV). SECM investigations were carried out in a 0.1 M KCl solution using a 3.5 μm radius carbon ultramicroelectrode (UME) as the SECM tip and a 25 μm radius platinum UME as the substrate electrode. Approach curves were recorded in the positive feedback mode of SECM by reducing Ru(NH3)63+ at the tip electrode and oxidizing Ru(NH3)62+ at the substrate electrode, as a function of the tip-substrate separation and S2O82- concentration. The one-electron reaction between electrogenerated Ru(NH3)62+ and S2O82- yields the unstable S2O83•-, which rapidly dissociates to produce highly oxidizing SO4•-. Because SO4•- is such a strongly oxidizing species, it can be further reduced at both the tip and the substrate, or it can react with Ru(NH3)62+ to regenerate Ru(NH3)63+. SECM approach curves display a complex dependence on the tip-substrate distance, d, due to redox mediation reactions at both the tip and the substrate. Finite element method (FEM) simulations of both SECM approach curves and SSV confirm a previously proposed mechanism for the mediated reduction of S2O82- using the Ru(NH3)63+/2+ redox couple. Our results provide a lower limit for dissociation rate constant of S2O83•- (∼1 × 106 s-1), as well as the rate constants for electron transfer between SO4•- and Ru(NH3)62+ (∼1 × 109 M-1 s-1) and between S2O82- and Ru(NH3)62+ (∼7 × 105 M-1 s-1).

Lightweight Potential of Anisotropic Plate Lattice Metamaterials

Additive manufacturing enables the production of lattice structures, which have been proven to be a superior class of lightweight mechanical metamaterials whose specific stiffness can reach the theoretical limit of the upper Hashin–Shtrikman bound for isotropic cellular materials. To achieve isotropy, complex structures are required, which can be challenging in powder bed additive manufacturing, especially with regard to subsequent powder removal. The present study focuses on the Finite Element Method simulation of 2,5D anisotropic plate lattice metamaterials and the investigation of their lightweight potential. The intentional use of anisotropic structures allows the production of a cell architecture that is easily manufacturable via Laser Powder Bed Fusion (LPBF) while also enabling straightforward optimization for specific load cases. The work demonstrates that the considered anisotropic plate lattices exhibit high weight-specific stiffnesses, superior to those of honeycomb structures, and, simultaneously, a good de-powdering capability. A significant increase in stiffness and the associated surpassing of the upper Hashin–Shtrikman bound due to anisotropy is achievable by optimizing wall thicknesses depending on specific load cases. A stability analysis reveals that, in all lattice structures, plastic deformation is initiated before linear buckling occurs. An analysis of stress concentrations indicates that the introduction of radii at the plate intersections reduces stress peaks and simultaneously increases the weight-specific stiffnesses and thus the lightweight potential. Exemplary samples illustrate the feasibility of manufacturing the analyzed metamaterials within the LPBF process.