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Nowadays, H13 steel is commonly utilized in processes such as pressure die casting, extrusion, and drop forging as well as in tube and glass manufacturing, however with limited manufacturability. The current research focuses on the effects of crucial process-related additive manufacturing parameters on the mechanical behavior of H13 hot work tool steel manufactured by the selective laser melting (SLM) additive manufacturing (AM) technique. The investigated parameters were the scan speed, the laser power, and their output volumetric energy density (VED) on the mechanical and morphological properties utilizing tensile and nanoindentation tests as well as scanning electron microscope (SEM). The results of this study revealed that it is possible to manufacture H13 steel parts with high mechanical performance using the SLM additive manufacturing technique. Specifically, the ultimate tensile strength reached a peak value at 1836 ± 48 MPa, while the yield tensile strength was found at 1468 ± 30 MPa. These parts are comparable to those made with conventional materials. A die part made using SLM was tested in the aluminum extrusion process on an 18 MN extrusion press producing acceptable quality extrudates and verifying its functionality and resilience under the high pressure and temperature of the extrusion process. [ABSTRACT FROM AUTHOR]

Recent advances in additive manufacturing (AM) led to the design and production of geometrical complex items with a plethora of materials facilitating the implementation of topology optimization procedures. Lattice designs are often employed for the topology optimization process providing lightweight structures with also additional advantages, such as high porosity and high surface area to volume ratio. Thus, there is a necessity to analyze these structures, examine their mechanical performance, and provide the computational tools to evaluate them. The current paper investigates the mechanical behavior of four triply periodic minimal surfaces (TPMS), namely the Schwarz primitive, the gyroid, the Schwarz diamond, and the Neovius structures. The structure of each lattice was analyzed and the influence of design-related parameters on the relative density was obtained. In order to study the mechanical response of the TPMS, representative volume elements (RVEs) were fabricated from polyamide 12 (PA12), utilizing the selective laser sintering (SLS) AM technique. Uniaxial quasi-static compression tests were conducted on the RVEs; their mechanical response was acquired and exploited in order to build advanced finite element (FE) models to simulate the overall mechanical behavior of each TPMS structure. Furthermore, these FE models with periodic boundary conditions were employed to extract the mechanical response of specimens that consisted of more unit cells. In addition, the numerical and the experimental data of more complex TPMS lattices were compared and verified the reliability and the accuracy of the FE models. Finally, the Schwarz diamond structure showed the best mechanical performance, in terms of strength and stiffness, slightly better than the Neovius, then followed the gyroid and Schwarz primitive structures, respectively. [ABSTRACT FROM AUTHOR]

Fused filament fabrication (FFF) is one of the fastest-growing additive manufacturing processes due to its low operational cost and the capability to rapidly construct prototypes with complex geometrical structures. Albeit the fact that FFF technology has been widely studied, the printing conditions that offer the optimum mechanical behavior exploiting numerical tools have not been systematically studied yet. The main goal of the study is to introduce machine learning-based models that predict the tensile strength of 3D printed parts and the development of an optimization tool, which infers the appropriate printing conditions according to the requirements of the user. The current paper deals with a computational and experimental study over the effect of specific process-related parameters and their impact on the mechanical behavior of an object manufactured via the FFF procedure. The data for the development of the predictive models and the numerical optimization module were acquired by manufacturing specimens with varying printing conditions. The results demonstrate that by adapting the suggested values on the printing parameters from the machine learning models and the optimization module, a remarkable enhancement on the mechanical properties of the printed parts can be achieved. Finally, the present work is the first to utilize machine learning-based regression models coupled with optimization techniques in order to improve the mechanical behavior of products manufactured via the FFF process. [ABSTRACT FROM AUTHOR]

The scientific fields of additive manufacturing and especially the extrusion-based technologies have gained immense attention in numerous commercial and research areas in the past decades. However, monitoring the manufacturing procedure and detecting errors during the process remain a technological challenge in the field. Generally, defect detection and dimensional accuracy inspection of the produced component is applied after the manufacturing has been completed and is accomplished via on-site manual monitoring. Hereupon, these approaches could affect the manufacturing production cost via the increase of feedstock material, waste parts, manpower, and machine rates. To overcome these issues, the present paper introduces a vision-based method to scan, filter, segment, and correlate in real-time the physical printed part with the digital 3D model as well as to evaluate the performance of the additive manufacturing process. More specifically, high-resolution point cloud data of the printed part are automatically captured, filtered, segmented, reconstructed, and compared with the corresponding digital 3D model in various stages of the procedure. Finally, the effectiveness of the suggested automatic monitoring and error detection methodology is experimentally validated. [ABSTRACT FROM AUTHOR]

Personal assistive robots to be realized in the near future should have the ability to seamlessly coexist with humans in unconstrained environments, with the robot's capability to understand and interpret the human behavior during human–robot cohabitation significantly contributing towards this end. Still, the understanding of human behavior through a robot is a challenging task as it necessitates a comprehensive representation of the high-level structure of the human's behavior from the robot's low-level sensory input. The paper at hand tackles this problem by demonstrating a robotic agent capable of apprehending human daily activities through a method, the Interaction Unit analysis, that enables activities' decomposition into a sequence of units, each one associated with a behavioral factor. The modelling of human behavior is addressed with a Dynamic Bayesian Network that operates on top of the Interaction Unit, offering quantification of the behavioral factors and the formulation of the human's behavioral model. In addition, light-weight human action and object manipulation monitoring strategies have been developed, based on RGB-D and laser sensors, tailored for onboard robot operation. As a proof of concept, we used our robot to evaluate the ability of the method to differentiate among the examined human activities, as well as to assess the capability of behavior modeling of people with Mild Cognitive Impairment. Moreover, we deployed our robot in 12 real house environments with real users, showcasing the behavior understanding ability of our method in unconstrained realistic environments. The evaluation process revealed promising performance and demonstrated that human behavior can be automatically modeled through Interaction Unit analysis, directly from robotic agents. [ABSTRACT FROM AUTHOR]