Yazar "Soudmand, Behzad Hashemi" seçeneğine göre listele

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    A finite element approach for addressing the interphase modulus and size interdependency and its integration into micromechanical elastic modulus prediction in polystyrene/SiO2 nanocomposites
    (Elsevier Sci Ltd, 2024) Soudmand, Behzad Hashemi; Biglari, Hasan; Fotouhi, Mohammad; Seyedzavvar, Mirsadegh; Choupani, Naghdali
    The perturbed transitional area between the nanoparticle and matrix shapes the properties of polymer nanocomposites. Due to the stochastic nature of these interphase regions, their size and physical properties are intricately linked. For instance, a higher interphase modulus, Eint, might result from a thinner interphase, and vice versa. The inherent randomness can introduce variability in the interphase modulus with respect to interphase thickness, tint. This challenges the practicality of conventional micromechanical approaches, which assume the interphase modulus to be either a constant or a function of filler and matrix properties when predicting the elastic modulus of polymer nanocomposites. Unlike conventional approaches, which simply used interphase quantification to predict global stiffness and treated the interphase modulus independently of its thickness, this study aims, for the first time, to consider the stochastic nature of the interphase, seeking to exclusively explore the interdependencies within the Eint - tint relationship in polystyrene/SiO2 nanocomposites. Simulations were conducted using finite element analysis, FEA, providing high accuracy and flexibility. To manage the large number of simulations, FEA was streamlined with a customized Python scripting, generating a spectrum of (Eint, tint) solutions for varying SiO2 contents based on experimental measurements and a rigorous methodology. Subsequently, empirical equations were formulated, unveiling the relationship between Eint and tintper composition. The FEA-driven interphase intercorrelation scheme was compared to the results obtained from a modified three-phase Halpin-Tsai model. Additionally, the FEA scheme was utilized to modulate the HT model by adjusting its relevant interphase terms.
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    Öğe
    Comparative performance analysis of optimization algorithms for hyperparameter tuning in LSBoost modeling of mechanical properties in FDM-printed nanocomposites
    (Pergamon-Elsevier Science Ltd, 2025) Seyedzavvar, Mirsadegh; Boga, Cem; Soudmand, Behzad Hashemi
    Hyperparameter tuning is essential for developing accurate machine learning models, yet its role in predicting the mechanical properties of 3D-printed nanocomposites remains underexplored. This study evaluates the performance of three optimization techniques-Bayesian Optimization (BO), Simulated Annealing (SA), and Genetic Algorithm (GA)-in tuning a Least Squares Boosting (LSBoost) model for predicting the mechanical properties of fused deposition modeling (FDM) 3D-printed polylactic acid (PLA)/silica (SiO2) nanocomposites. The properties assessed include modulus of elasticity (E), yield strength (Sy), and toughness at ultimate strength (Ku), influenced by key process parameters: extrusion rate (ER), SiO2 nanoparticle concentration (SC), deposition layer thickness (LT), infill density (ID), and infill geometry (IG). Tensile specimens were produced using a Taguchi L27 orthogonal array and tested under uniaxial tension. Tuning of the LSBoost model minimized a composite objective function involving root mean square error (RMSE) and (1-R2) loss metrics. Results demonstrated that GA achieved the best performance for yield strength prediction, with an RMSE of 1.9526 MPa and R2 of 0.9713, while BO yielded the highest R2 (0.9776) for modulus of elasticity prediction with a test RMSE of 130.13 MPa. For toughness, GA produced the lowest test RMSE of 102.86 MPa and the highest R2 of 0.7953 among the optimization methods. Generally, GA consistently outperformed BO and SA in optimizing the LSBoost model across most mechanical properties, highlighting its effectiveness for hyperparameter tuning in the context of FDM-fabricated nanocomposites.