Predicting elastic modulus of a porous structure is essential for applications in aerospace, biomedical, and structural engineering. Traditional methods often struggle to capture complex relationships between material properties, design variables, and mechanical behavior. This study employed artificial neural networks (ANNs) to predict the elastic modulus of a porous structure based on various material and design parameters. An ANN model was trained on a dataset generated via finite element analysis (FEA) simulations, covering diverse combinations of material properties and design variables (e.g., porosity, structure types). The model demonstrated high accuracy in predicting the elastic modulus on a separate test dataset. Key findings included identification of significant design variables influencing the elastic modulus and the ANN model"s ability to generalize predictions to new data. This approach showcases that ANN is a powerful tool for designing and optimizing porous structures, providing reliable mechanical property predictions without extensive experimental testing or complex simulations. The proposed method can enhance design efficiency and pave the way for developing advanced materials with tailored mechanical properties. Future research will extend the model to predict other mechanical properties and incorporate experimental validation to verify ANN predictions.

Recently, the demand for lightweight open-pore lattice structures with specific stiffness is increasing in many fields, such as the aeronautical, automotive, mechanical and bone tissue engineering sectors. For each concrete application, there is a need to predict its mechanical properties precisely and efficiently. There are several methods used to analyze the mechanical properties of lattice structures. Among them, the asymptotic expansion homogenization method is a more advantageous approach over the experimental, theoretical, and finite element methods, because it handles some of their limitations such as the time-consuming process, size effect, and the high amount of computational resources needed. Therefore, in this work, we use the asymptotic expansion homogenization method to perform a systematic parametric study to calculate the effective stiffness of different open-pore lattice structures. In addition, the designed models were fabricated using an SLA 3D printer, and the effective stiffness of the fabricated specimens was tested via UTM experiment to validate the numerical results computed by the asymptotic expansion homogenization method. Consequently, it was proved that this method is precise and effective for predicting the mechanical properties of lattice structures.

With recent development of 3D printing technology, its applications to the bio-industry are increasing. Many research studies are being done for manufacturing personalized tablets through this technology in the pharmaceutical process. In this study, to control the dissolution rate of tablets, a lattice structure was inserted into the tablet and the dissolution rate was compared. The tablet proposed in this study can be manufactured by the FDM method, adopting a lattice structure with a large surface area-to-volume ratio. Tablets containing various lattice structures were fabricated using water-soluble PVA filaments and dissolution experiments were conducted in water at 37oC. As a result, it was confirmed that the specific surface area and the mass loss rate were proportional to both the 3D lattice structure and the monolith structure. Among different structures, the diamond structure had the most active dissolution.

The collaboration of robots and humans sharing workspace, can increase productivity and reduce production costs. However, occupational accidents resulting in injuries can increase, by removing the physical safety around the robot, and allowing the human to enter the workspace of the robot. In preventing occupational accidents, studies on recognizing humans, by installing various sensors around the robot and responding to humans, have been proposed. Using the LiDAR (Light Detection and Ranging) sensor, a wider range can be measured simultaneously, which has advantages in that the LiDAR sensor is less impacted by the brightness of light, and so on. This paper proposes a simple and fast method to recognize humans, and estimate the path of humans using a single stationary 360° LiDAR sensor. The moving object is extracted from background using the occupied grid map method, from the data measured by the sensor. From the extracted data, a human recognition model is created using CNN machine learning method, and the hyper-parameters of the model are set, using a grid search method to increase accuracy. The path of recognized human is estimated and tracked by the extended Kalman filter.

In this investigation, we propose a simple and effective lateral shearing interferometer based on a polarization grating. In the lateral shearing device, an incident beam is split into two beams by a polarization grating, and the returning beams can be laterally shifted after reflecting off a flat mirror and passing through the polarization grating again. These two beams are not only laterally shifted, but also their polarization states are orthogonal to each other as circular polarizations. With a single image obtained by a pixelated polarization CMOS camera, the proposed LSI can obtain the phase map corresponding to the x-sheared interferogram, and the other phase map can be calculated from another single image obtained by 90° rotation of the shearing device. Then, the original wavefront corresponding to the surface figure of the specimen can be reconstructed by wavefront reconstruction algorithms. In the experiments, various wavefronts generated by concave mirrors and a deformable mirror were measured and compared with those of a commercial Shack-Hartmann sensor.

Nature-inspired architected materials have been widely used to achieve efficient structural materials by harnessing their cellular and hierarchical structures. For example, biological materials observed in bone, shell, nacre, and wood contain constituents, ranging from nanometers to centimeters, arranged in an ordered hierarchy. Because of their composited structures that contain micro and nanoscale building blocks arranged in an ordered hierarchy and the material size effect in the mechanical strength of nano-sized solids, bioceramic materials are mechanically robust and lightweight. The design principles offered by hard biological materials of multiscale composite structures can assist in the creation of advanced ceramic architectures. In addition, the evolution of additive manufacturing technologies has enabled the fabrication of materials with intricate cellular architected materials. In this review, we discussed advanced additive manufacturing for the fabrication of nature-inspired multiscale ceramic structures by combining conformal thin-film coating technique with conventional additive manufacturing methods.

Microlattice is well known as an efficient structure having a low density which maintains mechanical properties, so microlattice is being applied to the structural design of lightweight material in many industrial fields. In this study, we proposed a core-shell microlattice structure by the conformal coating of a metal nanoparticle-polymer composite in order to enhance the mechanical properties of polymeric microlattice printed by light-based 3D printing method. Polymeric architected microlattice was fabricated using digital light printing, which enabled the printing of complex structures with good surface smoothness. Then, the polymeric microlattice was conformally coated with aluminum nanoparticle-polymer composites. To investigate the effect of the metal nanoparticle-polymer composite coating on the mechanical properties of the microlattice, we studied the compressive behavior of cubic and octet-truss microlattices. As a result, we confirmed that both compressive strength and toughness of the two types of microlattices were effectively increased by coating with aluminum nanoparticle-polymer composites.

With the development of Additive Manufacturing process, lattice structures have recently been fabricated with fine quality. Lattice structures have unique performances which encompass various elastic responses. In this study, shear characteristics of the lattice structures (BCC and OTC) fabricated by SLM process, under optimized manufacturing conditions, were analyzed by 1/4 compression tests. As a result, several fracture modes and elastic configurations were found by comparing the compression test results of various lattice structures. In addition, the lattice structures possessed certain shear elasticity and normal elasticity among different types of lattices at elastic region when shearing. As the 1/4 compression test was simulating the lattice structure on concentrate load or shearing load, the test represented shock introspection characteristics of the lattice inner structure.

We studied compressive behavior of two types of lattice structures having small-scale struts fabricated by utilizing a metal additive manufacturing process. Generally known, the lattice structure has some advantages such as lightweight and high specific mechanical strength, allowing diverse potential applications in the aerospace and mobility industries. In this work, we proposed two types of lattice such as body-centered truss (BCT) and octahedral truss (OCT) that were designed and fabricated for a compression test. From the experimental results, the OCT has much higher strength than the BCT, and all cases showed several buckling modes during the compressive behavior. Furthermore, ‘restructuring’ occurred with BCT, and the compressive force increased overall but fluctuated due to the restructuring by an increase of compression. Through this work, we found out that the BCT has the interesting compressive behaviors, and a repetitive bucking-restructuring was found. In fact, its strength could be increased continuously by the restructuring during compression. In conclusion, the BCT has key-characteristics of lightweight and re-strengthening, which are applicable to various applications in the industry.

In this paper, finite element modeling methods for cylindrical composite lattice structures were verified through natural frequency test. Finite element models for cylindrical composite lattice structure were developed using beam, shell and solid elements. Natural frequency test was measured using impact test method under free-boundary condition. The analysis result of the beam element model showed up to 23% errors because the beam element could not consider the degradation of mechanical properties of non-intersection parts of the composite lattice structures. On the other hand, the natural frequencies of finite element analysis for shell and solid element models showed good results with natural frequencies test. From the analysis of the experiment, finite element model for composite lattice structures should use shell or solid element which takes into consideration the intersection and non-intersection parts.