AlSi12(Fe), AlSi10Mg(Fe), AlSi10MnMg, and AlMg4Fe2 die-casting alloys were produced by high-pressure die casting (HPDC) and vacuum-assisted high-pressure die casting (VADC) under a vacuum level of 200 mbar. The chemical composition, hardness, gas and shrinkage porosity, and mechanical properties were analyzed. The parts under study were subjected to a T6 heat treatment. The VADC led to a decrease in the percentage of defects in the as-cast state for all the alloys, due to a reduction in the amount of gas porosities. After heat treatment, the quantity of gas and shrinkage porosities increased. The efficiency and level of vacuum used were not sufficient to improve the mechanical properties in the as-cast state. The ductility of AlSi10Mg(Fe) and AlSi10MnMg alloys was improved after heat treatment; however, the YS and UTS of AlSi10Mg(Fe) did not increase. The primary aluminum alloys presented higher elongation values than the secondary aluminum alloys due to the reduced amount of the needle-like β-Al5FeSi phase.
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Real-time physically based rendering has long been looked at as the holy grail in Computer Graphics. With the introduction of Nvidia RTX-enabled GPUs family, light transport simulations under real-time constraint started to look like a reality. This paper presents Lift, an educational framework written in C++ that explores the RTX hardware pipeline by using the low-level Vulkan API and its Ray Tracing extension, recently made available by Khronos Group. Furthermore, to accomplish low variance rendered images, we integrated the AI-based denoiser available from the Nvidia ́s OptiX framework. Lift’s development arose primarily in the context of the graduate 3D Programming course taught at Instituto Superior Técnico and Master Theses focused on Real-Time Ray Trac- ing and provides the foundations for laboratory assignments and projects development. The platform aims to make easier students to learn and to develop, by programming the shaders of the RT pipeline, their physically-based ren- dering approaches and to compare them with the built-in progressive unidirectional and bidirectional path tracers. The GUI allows a user to specify camera settings and navigation speed, to select the input scene as well as the rendering method, to define the number of samples per pixel and the path length as well as to denoise the generated image either every frame or just the final frame. Statistics related with the timings, image resolution and total number of accumulated samples are provided too. Such platform will teach that nowadays physically-accurate images can be rendered in real-time under different lighting conditions and how well a denoiser can reconstruct images rendered with just one sample per pixel.
Real-time physically based rendering has long been looked at as the holy grail in Computer Graphics. With theintroduction of Nvidia RTX-enabled GPUs family, light transport simulations under real-time constraint startedto look like a reality. This paper presents Lift, an educational framework written in C++ that explores the RTXhardware pipeline by using the low-level Vulkan API and its Ray Tracing extension, recently made available byKhronos Group. Furthermore, to accomplish low variance rendered images, we integrated the AI-based denoiseravailable from the Nvidia ́s OptiX framework. Lift’s development arose primarily in the context of the graduate3D Programming course taught at Instituto Superior Técnico and Master Theses focused on Real-Time Ray Trac-ing and provides the foundations for laboratory assignments and projects development. The platform aims to makeeasier students to learn and to develop, by programming the shaders of the RT pipeline, their physically-based ren-dering approaches and to compare them with the built-in progressive unidirectional and bidirectional path tracers.The GUI allows a user to specify camera settings and navigation speed, to select the input scene as well as therendering method, to define the number of samples per pixel and the path length as well as to denoise the generatedimage either every frame or just the final frame. Statistics related with the timings, image resolution and totalnumber of accumulated samples are provided too. Such platform will teach that nowadays physically-accurateimages can be rendered in real-time under different lighting conditions and how well a denoiser can reconstructimages rendered with just one sample per pixel
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