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.
Soft robots, known for their flexible and gentle movements, have gained prominence in precision tasks and handling delicate objects. Most soft grippers developed thus far have relied on molding processes using high-elasticity rubber, which requires additional molds to produce new shapes, limiting design flexibility. To address this constraint, we present a novel approach of fabricating pneumatic soft grippers using thermoplastic polyurethanes (TPU) through the Fused Filament Fabrication (FFF) technique. The FFF technique enables the creation of various gripper shapes without the need for additional molds, allowing for enhanced design freedom. The soft grippers were designed to respond to applied air pressure, enabling controlled bending actions. To evaluate their performance, we conducted quantitative measurements of the gripper’s shape deformation under different air pressure conditions. Moreover, force measurements were performed during gripper operation by varying the applied air pressure and adjusting the mounting angle. The results of this study provide valuable insights into the design and control of soft grippers fabricated using TPU and the FFF process. This approach offers promising opportunities for employing soft robots in various fields and paves the way for further advancements in robotics technology.
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Directed energy deposition (DED) additive manufacturing technology enhances the functionality of existing or damaged parts by adding metallic materials to the surfaces. Blown-powder DED technology utilizes a focused, high-energy source to fuse the part’s surface with the supplied metal powder. Maintaining a constant stand-off distance (SOD), the distance between the deposition head and the workpiece, is a key factor in ensuring deposition quality, as variations in SOD will change the powder focus position and the laser spot size on the surface. Therefore, traditional additive manufacturing systems require CAD or pre-scanned surface data. In this study, we proposed auto-surface tracking technology. No workpiece CAD data or pre-scanned surface data are required, and in-situ measurement and feedback control can automatically consider the deposition height differences that cause a change in SOD when depositing the next layer. The accuracy of the SOD measurements and feedback control error was verified using a step height sample. The mean SOD measurement error was 4.7 ㎛ with a standard deviation of 42 ㎛ (reference SOD, 14 ㎜). The feasibility of the autosurface tracking technology was confirmed through the additive manufacturing processes of the gear and an actual blanking mold applied in the defense and industrial fields.
We propose a novel fin-tube expanding process using a spiral-grooved-expanding ball, prepared by the metal additive manufacturing process, to improve heat exchange performance in a fin-tube type heat exchanger. In this study, deformation of inner grooves in a tube, was minimized during the expanding process. For this, we developed lab-scale expanding equipment, and a spiral-grooved-expanding ball, was newly designed and fabricated. Comparing to a conventional tube expanding process, it was deduced that a deformation rate of groove height was reduced to approximately 8.3%, when the proposed process was used. Through this fundamental study, we validate that the developed process can be used to fabricate large-surface grooved tubes, for application to a high efficiency heat exchanger.
The additive manufacturing (AM) process is known to have a major influence on environmental impact. To find out AM process with lower environmental impact in the product manufacturing process, this study compares material extrusion (Fused Deposition Modeling, FDM), powder bed fusion (Laser Sintering, LS) and material jetting processes (Poly-Jet, PJ) for 200 NIST test artifacts, using data from the specification and software of three 3D printers (J750, P770 and uPrint SE Plus), the findings from various literature and Ecoinvent of SimaPro 8.4 database. The results showed that the effects of materials on the environment were the severest for LS (20.45 Pts) and the least for FDM (10.38 Pts) although the effects of power consumption on the environment were severest for FDM (126.91 Pts) and least for LS (20.18 Pts). To reduce the emission to environment in PJ and FDM, it is recommended to improve their printing speed and reduce power consumptions of waterjet and auxiliary equipment for support removal.
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