Faculty Publications
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Item 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 printed functionally graded foams response under transverse load(Elsevier B.V., 2023) Bonthu, D.; Mahesh, V.; Powar, S.; Doddamani, M.The applications of 3D printing are rapidly increasing in aerospace and naval applications. Nonetheless, 3D printing (3DP) of graded foams exhibiting property variation along the thickness direction is yet to be explored. In the current work, the different volume fractions of hollow glass micro balloon (GMB) reinforced high-density polyethylene (HDPE) composite based graded foams are 3D printed using the fused deposition modelling (FDM) technique. The bonding between successive layers and porosity distribution of these graded configurations are studied using micro-CT scan. Further, the 3D Printed functionally graded foams (FGFs) are tested for flexural response, and results are compared with numerical values. The micro-CT results showed delamination absence between the layers. In neat HDPE layers, porosity is not evident, while minor porosity creeps in the layers having the highest GMB content. Experimental results of the flexural test showed that the graded sandwiches exhibited better strength than the graded core alone. Compared to neat HDPE, the modulus of FGF-2 (H20–H40–H60) increased by 33.83%, implying better mechanical stiffness. Among all the FGFs, FGF-2 exhibited a better specific modulus. A comparative study of experimental and numerical results showed a slight deviation due to neglecting the induced porosity. © 2023 The Authors