NUST Institutions Library Catalogue Search for 'an:"129046"'

Metal 3D Printing of Functionally Graded Tri-Metallic Structure Using Wire Arc Additive Manufacturing (WAAM) /

By Ali, Syed Maisam Tammar . . 62p. , Wire Arc Additive Manufacturing (WAAM) is one of the promising metal additive manufacturing techniques being used for fabricating medium to large-scale parts and objects in various industries. During the last decade, WAAM has been used to produce parts with tailored properties by combining different materials in a single structure. Integrating different materials provides an opportunity to explore their efficacy in manufacturing functionally graded materials (FGM). Current research explores utilization of Gas Metal Arc Welding (GMAW) based WAAM in fabrication of Functionally Graded Tri-metallic Structure (FGTS). Individual as well as combination of three types of steels i.e. Mild steel (ER70S-6), Austenitic stainless steel (SS-316L) and High strength low alloy steel (AISI-5130) through WAAM was studied for this paper. Research validated that all three materials exhibited distinct microstructure which is in line with their individual alloy chemistry with smooth transition observed in FGTS. Tensile testing also demonstrated excellent integration of mechanical properties, with Ultimate Tensile Strength (442.48 MPa) and elongation (25.93%) representing a balance between strength and ductility. The research offers a novel avenue for producing trimetallic steel structures and widens the design opportunities for structures requiring sitespecific properties 30cm.

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3D Reconstruction using Machine Learning /

By Iqra Asghar. . 84p. , The field of 3D reconstruction has undergone significant advancements with the integration of machine learning techniques, enabling more efficient and accurate modeling of complex environments. This research focuses on leveraging cutting-edge methodologies, such as 3D Gaussian Splatting, to address challenges in real-time rendering and dynamic scene reconstruction. Traditional methods for dynamic scene rendering often fall short in maintaining high-quality and real-time performance, especially for complex, moving environments. In contrast, Gaussian Splatting employs probabilistic primitives to represent 3D point clouds, offering a balance between computational efficiency and visual quality. This study extends Progressive Gaussian Splatting to dynamic environments by introducing a framework that ensures temporal coherence and real-time performance. The proposed methodology employs a hybrid geometric representation, progressive propagation for Gaussian refinement, and deformation fields encoded via multi-resolution voxel grids to capture motion. Evaluations on synthetic and real-world datasets demonstrate significant improvements in rendering quality and temporal coherence, achieving state-of-the-art results in metrics such as PSNR (41.99), SSIM (0.995), and LPIPS (0.011) for synthetic dataset and increased PSNR by 1.79 and SSIM by 0.046 for real world’s hypernerf dataset. The research provides new insights into the practical application of Gaussian Splatting in dynamic environments, opening avenues for enhanced virtual reality (VR), augmented reality (AR), and robotics applications. 30cm.

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ENHANCED NOVEL VIEW SYTHESIS VIA DEEP LEARNING-BASED 3D GAUSSIAN SPLATTING /

By Ullah Noor, Zabeeh . . 83p. ; , 3D Gaussian Splatting (3DGS) has emerged as a breakthrough in explicit radiance fields and computer graphics which has enabled precise scene representation, real time rendering, and efficient novel view synthesis. This paper explores the evolution of 3D rendering and recent advancements in 3DGS, with a particular focus on different techniques for synthesizing novel views with the incorporation of deep learning architectures especially transformers. To enhance scene quality, this research investigates the integration of monocular depth information during rendering and refines the loss function to improve reconstruction accuracy. By incorporating depth information our method enhances geometric details by capturing intricate details and reduction of artifacts. The findings contribute to the reconstruction of 3D scene with high fidelity, offering insights to optimize Gaussian Splatting technique for more efficient and realistic 3D rendering applications. 30cm..

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