Faculty Publications
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Item Compressive and flexural properties of functionally graded fly ash cenosphere-epoxy resin syntactic foams(John Wiley and Sons Inc, 2015) Doddamani, M.; Kishore; Shunmugasamy, V.C.; Gupta, N.; Vijayakumar, H.B.The present study focuses on developing functionally graded syntactic foams (FGSFs) based on a layered co-curing technique. The FGSFs were characterized for compressive and flexural properties and compared with plain syntactic foams. The results showed that the specific compressive modulus was 3-67% higher in FGSFs compared to plain syntactic foams. FGSF exhibited 5-34% and 34-87% higher specific modulus and strength, respectively in flexural mode. The microscopic examinations of comparative responses of the filler and matrix to deformation suggest that the failure is dominated by the matrix. The gradient in the composition of syntactic foams helps in effectively distributing the stress throughout the microstructure and results in improved mechanical performance of syntactic foams. From the microscopy studies, it is evident that, the failure mechanism in the FGSF under flexural loading is governed by a crack that initiated on the tensile side of the specimen and propagated through the thickness to cause complete fracture. The microscopic observations further clearly demonstrate the existence of seamless interfaces between the layers and a clear difference in the cenosphere concentration across the interface, affirming the gradation in the prepared samples. The results show that appropriate compositions of FGSFs can be selected to develop materials with improved mechanical performance. © 2014 Society of Plastics Engineers.Item Effect of arctic environment on flexural behavior of fly ash cenosphere reinforced epoxy syntactic foams(Elsevier Ltd, 2018) Garcia, C.D.; Shahapurkar, K.; Doddamani, M.; Mohan Kumar, G.C.M.; Prabhakar, P.In this paper, the effect of arctic conditions on the flexural response of cenosphere/epoxy syntactic foams is investigated. Understanding the behavior of such foams under extreme conditions is critical for exploring their suitability for constructing lightweight platforms used in arctic explorations. Such platforms are exposed to subzero temperatures for extended periods of time potentially degrading their mechanical properties. In the research study presented here, samples of cenosphere/epoxy syntactic foams were conditioned under arctic environment at ?60 °C temperature for a period of 57 days. Flexural tests were then conducted at room temperature as well as in-situ ?60 °C on the conditioned samples and compared against unconditioned samples. Combinations of surface modification and cenosphere volume fractions were considered. Experimental findings showed that an increase in flexural modulus can be observed at room temperature with increasing cenosphere volume content for both untreated and treated cenosphere reinforced syntactic foams. In contrast, a decrease in flexural strength was observed as compared to neat resin. For the case of arctic exposed samples, an apparent increase in flexural modulus was recorded between 7-15% as compared to room temperature cenospheres/epoxy syntactic foams. In addition, an apparent increase of 3–80% in the flexural strength was observed under arctic environment. The conditioning of cenosphere/epoxy syntactic foams under low temperatures manifested lower strains to failure as compared to neat epoxy and they exhibit quasi-brittle behavior leading to sudden failure in the post peak regime. © 2018 Elsevier LtdItem Mechanical response of additively manufactured foam: A machine learning approach(Elsevier B.V., 2022) Neelam, R.; Kulkarni, S.A.; Bharath, H.S.; Powar, S.; Doddamani, M.This paper uses ensemble and automated machine learning algorithms to predict the mechanical properties (tensile and flexural strength) of a three-dimensionally printed (3DP) foamed structure. The closed cell foams were made from the most commonly used thermoplastic, High-Density Polyethylene (HDPE). The hollow glass microspheres are infused in HDPE at varying volume %. The available data on these foams' mechanical properties are used by the chosen machine learning (ML) algorithms to propose the best suited algorithm for such a three-phased microstructure as these closed cell foams exhibit. Finally, the strength predictions from the models were validated using experimental data. The models were trained with nozzle temperature, bed temperature, and force values as input parameters. The output parameters predicted were the tensile and flexural strength. LightGBM outperforms all other models in terms of performance among ensemble-based models, while H2OAutoML outperforms all other models. All the ML algorithms produced models with greater than 95% accuracy. Finally, memory and time consumption for each model are presented. © 2022 The AuthorsItem 3D printing of functionally graded nanocomposites: An investigation of microstructural, rheological, and mechanical behavior(John Wiley and Sons Inc, 2024) Kumar, S.; Rajath, S.; Shivakumar, N.D.; Ramesh, M.R.; Doddamani, M.Manufacturing functionally graded material through 3D printing is challenging owing to the deposition of different materials with different thermal properties in each layer, leading to a higher thermal gradient between deposited and depositing layers, resulting in improper bonding between them and, hence, reduced mechanical properties. This study focuses on 3D printing of functionalized multi-walled carbon nanotubes (MWCNTs)/high-density polyethylene (HDPE)-based lightweight functionally graded nanocomposites (FGNCs) and their investigation for microstructural, rheological, physical, and mechanical properties. Functionalized MWCNTs (0.5% → 5%) are initially compounded with widely utilized HDPE to develop nanocomposites (H0.5→H5 pellets) for extruding filaments for 3D printing. 3D-printed FGNC samples are investigated through scanning electron microscopy (SEM), rheology, density, tensile, and flexural tests. SEM and rheology confirm the homogeneous dispersion of the filler in HDPE and the processing parameters suitability in blending, extrusion, and 3D printing. Complex viscosity (η*), loss modulus (E″), and storage modulus (E′) of FGNCs increase, while the damping decreases with the MWCNTs rise in the graded layers. Density results revealed the highest weight saving potential (~12%) of FGNC-2 (H1–H3–H5), showing great weight saving potential. Tensile and flexural properties rise when the MWCNTs content rises in the graded layer. The FGNC-2 showed the highest tensile strength and moduli, 37.12% and 90.41% higher than HDPE. Flexural strength and moduli are also found to be the highest for FGNC-2, 28.57%, and 26.83% higher than HDPE. The highest specific moduli and strength are found for FGNC-2, 46.16% and 44.14% higher than HDPE, respectively. Experimental findings are found to be strongly in agreement with numerical findings. 3D-printed FGNC-2 demonstrated the best flexural and tensile characteristics with the lowest weight and hence can be used to make practical parts and structures that need variable stiffness. Highlights: FGNCs functionally graded n anocomposites are concurrently 3D printed. FGNC-2 exhibited the highest weight saving potential of 12%. FGNC-2 showed 90.41% and 37.12% enhanced tensile modulus and strength. FGNC-2 displayed 28.57% and 26.83% improved flexural strength and modulus. FGNCs exhibited better mechanical performance than the homogeneous NCs. © 2024 Society of Plastics Engineers.