Saudi Cultural Missions Theses & Dissertations

Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10

Browse

Search Results

Now showing 1 - 2 of 2
  • ItemRestricted
    Biaxial flexural strength of lithium-based CAD/CAM dental glass-ceramics
    (univirsity of florida, 2025) Alhindi, Saleh; Abdulhameed, Nader; Mateus, Rocha; Roulet, Jean-Francois
    Abstract: Objective: The aim of this study was to evaluate the biaxial flexural strength of four CAD/CAM dental glass-based ceramics containing lithium in the crystalline structure. Material and methods: A universal testing machine is used to evaluate the biaxial flexural strength of four groups of ceramic materials: IPS e.max CAD, GC LiSi Block, CEREC Tessera, and unbranded. Each group contains 20 samples, for a total of 80 samples. In addition to that, the microstructure was examined using SEM. Results were analyzed using one-way ANOVA followed by the Tukey test for multiple comparisons, with statistical significance set at p < 0.05 and a 95% confidence interval. Weibull Analysis was used to assess the biaxial flexural strength and was also based on the 66% log-likelihood parameter for the different ceramics. Result: CEREC Tessera demonstrated the highest BFS, while GC LiSi Block was the lowest. There were no statistically significant differences between IPS e.max CAD, CEREC Tessera, and unbranded (p>0.05), whereas GC LiSi Block exhibited significant differences (p<0.05). The chemical composition and microstructure of the tested samples varied significantly, including the presence of zirconium oxide (ZrO₂) in IPS e.max CAD and distinct crystal morphologies across the materials. Conclusion: Statistically significant differences in biaxial flexural strength (BFS) were observed between GC LiSi Block and the other ceramic groups, with GC LiSi Block exhibiting the lowest flexural strength (p < 0.05). Additionally, the chemical composition and microstructure of the tested samples varied significantly. Therefore, both null hypotheses of this study were rejected. From a clinical perspective, despite these variations, all tested materials exceeded the ISO 6872:2015 threshold of 300 MPa, confirming their suitability for various restorative applications.
    9 0
  • Thumbnail Image
    ItemRestricted
    The Effects of Combined Heat-Assisted Additive Manufacturing and Laser Re-melting on Martensitic Stainless Steel
    (OhioLINK Electronic Theses and Dissertations Center, 2023-12) Ali, Majed Saleh; Qattawi, Ala
    Laser powder bed fusion (LPBF) has become an attractive manufacturing method due to its ability to fabricate metals with complex designs. LPBF can build accurate parts with geometries that are not possible in traditional manufacturing processes. Many applications of LPBF such as dental implants, replacement prosthetic joints, and gas turbine engines have demonstrated significant improvement in mechanical properties and microstructure. However, to qualify the LPBF process as an industry standard, more research and testing are necessary using different parameters to understand the influencing factors of the material properties. In LPBF, the produced parts require post-processing. In most cases, the fabricated part needs heat treatment to enhance the microstructure and mechanical properties and achieve better homogenous material. Some applications need post- processing to reduce surface roughness. These needs have motivated many research studies on how to improve material strength and obtain the desired surface roughness for LPBF materials in general. In addition, the quality and mechanical properties of the materials must be considered. The disadvantage of LPBF is the formation of internal stresses. When the laser fuses a layer, the rapid cooling results in residual stress as the laser moves across its path. Laser remelting with a heat-assisted additive manufacturing process can improve the surface quality and relieve residual stresses; however, if the laser power and laser speed are not suitable, it can reduce the strength and hardness of the material which was within the range of typical wrought properties. There is a lack of understanding of the effect of laser remelting and its parameters on the resultant material properties and any improvement that can be achieved for LPBF for metals such as steel. The integration of laser re-melting and heat-assisted additive manufacturing is also not fully understood in terms of its contribution to improving materials' microstructure and reduction in the high residual stresses usually formed during LPBF. The first objective of this dissertation is to investigate the effects of laser re-melting on stainless steel material fabricated by heat-assisted additive manufacturing in terms of mechanical properties. The second objective focuses on identifying the effects of laser re- melting on stainless steel fabricated by heat-assisted additive manufacturing to study the phase transformation and the formation of martensite in the stainless-steel material as the phase transformation is affected by the cooling rate. The retained austenite results in anisotropy of the mechanical properties. It was found the heat-treated samples have a reduction in the amount of retained austenite, homogeneous grain structure, and better mechanical properties. The transformation of the martensite-austenite phase was observed between 533 °C and 928 °C for austenite in heating, and between 175 °C and 74 °C for martensite in cooling. The martensite changes into reverted austenite if the heating is below the austenite phase starting temperature. The third objective is to predict the phase transformation in additively manufactured martensitic steel through a neural network with experimental validation. Retained austenite in martensitic steel is influenced by various temperature and time parameters. An increase in retained austenite tends to reduce the strength of the material. Neural networks are among the most dominant machine learning approaches used for their ability to fit into various classification or regression problems. In this context, the neural network is employed to predict the retained austenite based on post-processing heat treatment process parameters.
    72 0

Copyright owned by the Saudi Digital Library (SDL) © 2025