3D Systems (Belgium)

The AMD Ryzen 7040 series is the first x86 processor with an integrated neural processing unit (NPU). The AMD XDNA technology in the NPU of Ryzen artificial intelligence (AI) processors provides optimized compute and memory resources for the needs for AI workloads and a specialized data movement architecture that allows to minimize bandwidth requirements leading to higher performance and efficiency. Across a selection of neural network benchmarks, XDNA provides between 4.3× and 33× better performance per watt, leading to extended battery life. The AI-optimized capabilities of the Ryzen 7040 NPU enable new AI experiences that are not possible without XDNA, making it a fundamental component in today’s Ryzen-AI-powered devices and setting the foundation for an exciting roadmap toward future AI capabilities in mobile PCs.

An increasing demand for additively manufactured polymer composites with optimized mechanical properties is manifesting in different industries such as aerospace, biomedical, and automotive. Laser sintering (LS) is an additive manufacturing method that has the potential to produce reinforced polymers, which can meet the stringent requirements of these industries. For the development of a commercially viable LS nylon-based composite material, previous research studies worldwide have focused on adding glass beads to the powder material with the goal to produce fully dense parts with properties more representative of injection molded (IM) thermoplastic composites. This led to the development of a commercially available glass bead-filled polyamide 12 (PA12) powder. Although this powder has been on the market for quite a while, an in-depth comparison of the mechanical behavior of laser sintered versus IM glass bead-filled PA12 is lacking. In this study, laser-sintered glass bead-filled PA12 samples were built in different orientations and compared to IM counterparts. After sample production, the mechanical performance of the produced LS and IM parts was tested and compared to evaluate the quasistatic and dynamic mechanical performance and failure mechanisms at different load levels. In addition, the glass bead-filled PA12 properties were also compared to those of standard (unfilled) LS PA12 to assess whether glass beads actually improve the mechanical performance and fatigue lifetime of the final LS samples, as suggested in literature. Results in this work present and explain the increased stiffness but decreased fatigue life of glass bead-filled polyamide parts made by LS and IM. This research can be regarded as a "benchmark" study, in which samples produced from commercially available, filled and unfilled, PA12 powder grades are compared for both LS and conventional production techniques.

Laser powder bed fusion (L-PBF) is an additive manufacturing (AM) process which offers several advantages over conventional manufacturing techniques, including material savings and design possibilities. For these reasons several industrial sectors such as biomedical, aerospace and automotive, are considering L-PBF for customized parts production. Among the processable material palette, Ti6Al4V gained increasing interest due to the high specific strength, and stiffness, and excellent bio-compatibility, and corrosion resistance. However, to include L-PBF Ti6Al4V parts in load bearing applications it is important to properly understand the mechanical properties of AM’ed materials with a specific focus on fatigue behaviour. In this study the fatigue notch sensitivity of Ti6Al4V L-PBF manufactured specimens is investigated in depth. Samples with different notches were subjected to stress relieving heat treatment to generate a fine α+β microstructure. The results were used to define a relation between the perturbed stress field generated by the geometrical discontinuity, the microstructure size and the notch sensitivity. Moreover, the critical distance has been calculated using the theory of critical distance formulated with the line method (LM). The estimated value were adopted to predict fatigue properties of notched components with different notch radii.

In this study, we present the first results of a newly developed melt pool monitoring tool for selective laser melting, called DMP-meltpool. A manual data analysis method is given, and the events indicated by the analysis (DMP-meltpool events) are shown to correlate to the static tensile properties of the samples built. These events indicate the probability of material discontinuities (defects) in the metal additive manufacturing (AM) parts. In order to do so, cylindrical bars of Ti-6Al-4V ELI were built and monitored using DMP-meltpool. The tensile properties of the printed cylinders were correlated with the events detected by DMP-meltpool. An inverse relation between plastic elongation and the DMP-meltpool event density was observed. These results show that DMP-meltpool can be used to predict the quality of AM parts by detecting variations in the signals and tagging these events throughout the build as defects. Thus the technique can be employed for first stage in-line quality control of AM parts and for sorting out parts with potential defects non-destructively. The DMP-meltpool events could have significant correlations with other mechanical properties (like fatigue, hardness, fracture toughness, and crack propagation) since such properties are influenced by defects originating from the process instabilities.