Engineering topics

Ferdousi, Sanjida

Publications and source records attributed to Ferdousi, Sanjida.

3 records

DOE OSTI2024

Distinct Mechanism of Anti-Corrosion and Swelling-Adhesion Modeling of Low-Dimensional Nylon-Fluoropolymer Composite Coatings

Compared to other polymers, composite coatings with fluoropolymers as the matrix have attracted considerable interest due to their mechanical performance, chemical stability, and low surface energy. Herein, we present an anticorrosion mechanism of fluoropolymer composite coatings achieved by dispersing varying amounts of polyamide 12 (PA-12) particles over a poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) matrix. The choice of PVDF-HFP as a coating matrix was driven by its superior processability and mechanical properties over Teflon, while PA-12 afforded swelling capacity, preventing electrolyte permeation, and inhibiting matrix stress crack propagation. The corrosion resistance of the coatings was assessed mainly by potentiodynamic polarization and impedance measurements while being immersed in a NaCl solution. Our electrochemical measurement findings showed that 0.75% w/w PA-12 in the PVDF-HFP matrix significantly enhanced corrosion protection even after 21 days of immersion. Microscopy, dielectric spectroscopy, surface analysis, and mechanical testing (American Society for Testing and Materials standards) corroborated the results. PVDF-HFP coatings containing a minimum percolation threshold of 0.75 wt % PA-12 with 1 mil thickness on mild steel exhibited a remarkable increase in resistance and film adhesion, and a notable corrosion rate decrease. Finite element analysis simulation with a bilinear traction-separation law confirmed the swelling mechanism and adhesion behavior. Finally, this study highlights the potential of nylon particle dispersion in PVDF-HFP matrices as a practical approach to developing advanced anticorrosion coatings with promising practical applications in various industries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Investigation of 3D printed lightweight hybrid composites via theoretical modeling and machine learning

Hybrid composites combine two or more different fillers to achieve multifunctional or advanced material properties, such as lightweight and enhanced mechanical properties. The properties of the composites significantly depend on their microstructures, which can be tailored via advanced 3D printing processes. Understanding the process-structure-property relationships is critical to enable the design and engineering of novel hybrid composites for applications in aerospace, automotive, and protective coatings. Here, for this work, we develop 3D printable and lightweight hybrid composites and leverage the conventional design of experiments, a theoretical hybrid model, and an image-driven machine learning (ML) method to investigate their mechanical behaviors. The hybrid composites are formulated with elastomer matrix, microfillers, and thin-shell particles, enabling a significant degree of design freedom of microstructures with densities and mechanical properties varying up to 70% and 91%, respectively. Our statistical analysis indicates that the 3D printing path direction and the microfibers fraction are dominating process parameters with contribution percentages of 45.3% and 57.7% on the specific stiffness and strength, respectively. A hybrid mechanics model is developed based on a simple Weibull distribution function and classical single-filler models to effectively capture the variations in mechanical properties, however, it overestimates the values due to its statistical constraints and idealization of experimental uncertainty. The image-driven ML model leverages the microscale images directly without losing the structural details, shows more accurate predictions with experimental data, and has 48.6% lower root mean square error than the theoretical model.

3D printing↗

DOE OSTI2021

Characterize traction–separation relation and interfacial imperfections by data-driven machine learning models

Abstract Interfacial mechanical properties are important in composite materials and their applications, including vehicle structures, soft robotics, and aerospace. Determination of traction–separation (T–S) relations at interfaces in composites can lead to evaluations of structural reliability, mechanical robustness, and failures criteria. Accurate measurements on T–S relations remain challenging, since the interface interaction generally happens at microscale. With the emergence of machine learning (ML), data-driven model becomes an efficient method to predict the interfacial behaviors of composite materials and establish their mechanical models. Here, we combine ML, finite element analysis (FEA), and empirical experiments to develop data-driven models that characterize interfacial mechanical properties precisely. Specifically, eXtreme Gradient Boosting (XGBoost) multi-output regressions and classifier models are harnessed to investigate T–S relations and identify the imperfection locations at interface, respectively. The ML models are trained by macroscale force–displacement curves, which can be obtained from FEA and standard mechanical tests. The results show accurate predictions of T–S relations ( R 2 = 0.988) and identification of imperfection locations with 81% accuracy. Our models are experimentally validated by 3D printed double cantilever beam specimens from different materials. Furthermore, we provide a code package containing trained ML models, allowing other researchers to establish T–S relations for different material interfaces.

97 MATHEMATICS AND COMPUTING↗