Reza Barbaz Isfahani holds an M.Sc. in Applied Design Mechanical Engineering from Iran University of Science and Technology (2013), and a Ph.D. in Applied Design Mechanical Engineering from Amirkabir University of Technology (2021), all with distinction. His research focuses on composites/nanocomposites, smart/self-healing materials, mechanical property prediction, finite element modeling, and non-destructive testing. Key achievements include pioneering electrosprayed multi-core microcapsules for self-healing polymers, hybrid MWCNT/nano-SiO₂ reinforcement enhancing elastic modulus, and patenting multi-scale composites. He has authored >30 ISI papers on nanocomposite mechanics and multi-scale simulations validated experimentally.
Articles
Smart Science (23080477)13(1)pp. 60-76
This paper presents a comprehensive investigation into the predictive modeling of eco-friendly composite materials reinforced by Abaca fibers, Halloysite Nanotubes (HNT), and Egg Shell Powder (ESP) additives. Deep Neural Networks (DNNs) were employed to capture the complex behaviors of these materials under various testing conditions, including Cone Calorimeter Tests (CCT), micro-indentation with Vickers and Conical indenters, and three-point bending tests. The trained DNNs exhibited remarkable accuracy in predicting critical parameters such as Heat Release Rate (HRR), Average Rate of Heat Emission (ARHE), Total Heat Release (THR), Total Smoke Production (TSP), and Total Oxygen Consumption (TOC) during CCT. Additionally, the DNNs successfully replicated force-depth diagrams from Vickers and Conical indentations, showcasing their proficiency in modeling loading and unloading profiles. Furthermore, the flexural responses during three-point bending tests were accurately predicted, encompassing flexural modulus, the maximum flexural stress, strain at break, and energy. Validation through metrics such as Mean Squared Error (MSE) and coefficient of determination (R2) demonstrated the reliability of the DNNs in capturing material behaviors. Consequently, the study showcases the potential of machine learning, particularly DNNs, as a robust tool for predictive modeling in material science so that R2 values below 0.995 were not obtained for the trained DNNs as well as MSEs in the order of e−4. © 2024 Ali Khalvandi, Saeed Kamarian, Reza Barbaz-Isfahani, Saeed Saber-Samandari and Jung-Il Song.
Ultrasonics (18749968)138
The formation of multiple delaminations is a frequently observed damage mechanism in composite materials, exerting a more pronounced influence on their strength properties compared to single delaminations. To tackle this issue, the incorporation of nanoparticles has been investigated as a means to enhance composite materials. This study aims to examine the effects of nano-additives, specifically carbon nanotubes and nanosilica, on the flexural behavior of glass/epoxy composites containing multiple embedded delaminations. The acoustic emission technique is employed to gain deeper insights into the damage mechanisms associated with flexural failure. Artificial delaminations of varying sizes, arranged in a triangular pattern, were introduced into four interlayers of a [(0/90)2]s oriented glass/epoxy composite. The findings reveal a notable reduction in flexural properties due to the presence of multiple delaminations. However, the addition of nanoparticles demonstrates a significant improvement in the flexural behavior of the multi-delaminated specimens. The most substantial enhancement is observed in the composite incorporating 0.3 wt% nanosilica + 0.5 wt% carbon nanotubes. Furthermore, genetic K-means and hierarchical clustering techniques are employed to classify different damage mechanisms based on the peak frequency and amplitude of the acoustic emission signals. The results indicate that the hierarchical clustering method outperforms the genetic K-means method in accurately clustering the acoustic emission signals. Moreover, the incorporation of nanoparticles' impact on the occurrence of distinct damage mechanisms is evaluated through the analysis of acoustic signals using Wavelet Packet Transform. By investigating the flexural behavior of nanomodified multi-delaminated composites and employing the acoustic emission technique, this study offers valuable insights into the role of nanoparticles in enhancing the mechanical properties and monitoring the damage mechanisms of composite materials. © 2024 Elsevier B.V.
Mirmohammadi, H.,
Iranmanesh, P.,
Barbaz isfahani, R.,
Arzani, S.,
Kolahi, J.,
Dummer, P. Dental Hypotheses (21558213)15(4)pp. 53-54
Mechanics of Advanced Materials and Structures (15210596)31(12)pp. 2581-2594
In this study, we promote a multi-scale modeling to predict the healing efficiencies (HEs) of incorporated polymers with self-healing microcapsules. The Python scripts were employed to generate three representative volume elements (RVEs) with randomly dispersed 5, 7.5, and 10% volume fraction (VF) of alginate microcapsules. Three VUSDFLD subroutines were codded and supplemented with ABAQUS/Explicit solver to obtain the maximum tensile stresses (Sut) of virgin, damaged, and healed samples followed by calculating HF of self-healing polymers. Based on the simulation results, more incorporation of self-healing microcapsules increased the tensile after impact HF, so that HFs were increased from 46.31% for RVEs containing 5% VF up to 65.41% and 84.84% for 7.5% and 10% VF, respectively. The presence of more self-healing microcapsules could improve the chance of rupturing more filled microcapsules with healing agents after crack propagation due to impact damages in the matrix. Thus, more damaged elements would be healed by spread healing agents. To evaluate the reliability of simulation results, the specimens containing electrosprayed multicore self-healing microcapsules were fabricated, and experimental HFs were calculated. The same trend was obtained for experimental results, as acquired in the simulation of RVEs. The error of healing efficiencies were only 6.82%, 2.81%, and 7.74 for incorporated specimens with 5, 7.5, and 10% VF of electrosprayed multicore microcapsules, respectively, indicating the accuracy of introduced multi-scale finite element modeling. The fabrication defects of experimental specimens can be the reason of simulation errors. © 2023 Taylor & Francis Group, LLC.
Advances in Nano Research (2287237X)16(2)pp. 127-140
Upon direct/indirect exposure to flame or heat, composite structures may burn or thermally buckle. This issue becomes more important in the natural fiber-based composite structures with higher flammability and lower mechanical properties. The main goal of the present study was to obtain an optimal eco-friendly composite system with low flammability and high thermal buckling resistance. The studied composite consisted of polypropylene (PP) and short abaca fiber (AF) with eggshell powder (ESP) and halloysite clay nanotubes (HNTs) additives. An optimal base composite, consisting of 30 wt.% AF and 70 wt.% PP, abbreviated as OAP, was initially introduced based on burning rate (BR) and the Young’s modulus determined by horizontal burning test (HBT) and tensile test, respectively. The effects of adding ESP to the base composite were then investigated with the same experimental tests. The results indicated that though the BR significantly decreased with the increase of ESP content up to 6 wt.%, it had a very destructive influence on the stiffness of the composite. To compensate for the damaging effect of ESP, small amount of HNT was used. The performance of OAP composite with 6 wt.% ESP and 3 wt.% HNT (OAPEH) was explored by conducting HBT, cone calorimeter test (CCT) and tensile test. The experimental results indicated a 9~23 % reduction in almost all flammability parameters such as heat release rate (HRR), total heat released (THR), maximum average rate of heat emission (MARHE), total smoke released (TSR), total smoke production (TSP), and mass loss (ML) during combustion. Furthermore, the combination of 6 wt.% ESP and 3 wt.% HNT reduced the stiffness of OAP to an insignificant amount by maximum 3%. Moreover, the char residue analysis revealed the distinct differences in the formation of char between AF/PP and AF/PP/ESP/HNT composites. Afterward, dilatometry test was carried out to examine the coefficient of thermal expansion (CTE) of OAP and OAPEH samples. The obtained results showed that the CTE of OAPEH composite was about 18% less than that of OAP. Finally, a theoretical model was used based on first-order shear deformation theory (FSDT) to predict the critical bucking temperatures of the OAP and OAPEH composite plates. It was shown that in the absence of mechanical load, the critical buckling temperatures of OAPEH composite plates were higher than those of OAP composites, such that the difference between the buckling temperatures increased with the increase of thickness. On the contrary, the positive effect of CTE reduction on the buckling temperature decreased by raising the axial compressive mechanical load on the composite plates which can be assigned to the reduction of stiffness after the incorporation of ESP. The results of present study generally stated that a suitable combination of AF, PP, ESP, and HNT can result in a relatively optimal and environmentally friendly composite with proper flame and thermal buckling resistance with no significant decline in the stiffness. Copyright © 2023 Techno-Press, Ltd.
