Logesh Shanmugam

The future of space exploration requires a paradigm shift from deployable pre-manufactured structures to structures that are manufactured and assembled on-orbit. A demonstration of onorbit manufacturing of high stiffness carbon fiber composites is sought. However, space-based composite manufacturing is significantly constrained by launch volume, on-orbit processing energy and equipment, and off-gassing requirements. While others have used on-orbit thermoplastic additive manufacturing, this study investigates the processing and properties of encapsulated thermoset carbon fiber prepreg with the potential for on-orbit manufacturing of composite tubes. Specifically, we infuse catalyzed dicyclopentadiene (DCPD) resin into layered cylindrical carbon fiber preform sleeves, seal and encapsulate the prepregs between compliant and impermeable films, and then mold and cure the prepreg to rapidly produce a thin-walled, high-stiffness composite tubes using a low energy process with low outgassing. We demonstrate the scalability of tube prepreg by infusing a 10-foot long prepreg with industrial partners. Using flat surrogates of the cylindrical laminate, we demonstrate that the prepregs are stable and can be cured quickly (~ 5 minutes) and with only 11 psi (75.8 Kpa) of applied pressure to create composites with high 􀜶􀯚 (155-160°C), low voids (<1%), and low off-gassing (<1%). Initial promise is demonstrated for a future planned space demonstration on the International Space Station (ISS) through “Mission Illinois”.

A novel approach to determine the translaminar crack resistance curve of composite laminates by means of a machine learning model is presented in this paper. The main objective of the proposed method is to extract hidden information of crack resistance from strength values of center-cracked laminates. Compared to traditional measurements, the notable advantage is that only tensile strength values are required which can be obtained by a rather simpler experimental procedure. This is achieved by the incorporation of the finite fracture mechanics, which links crack resistance with strength values. In order to get training dataset, a semi-analytical method using both finite element method and finite fracture mechanics is employed to generate strength values of center-cracked specimens with different random R-curves, which serve as inputs for our artificial neural network. Regarding the outputs, principal component analysis is performed to reduce dimensionality and find suitable descriptors for crack resistance curves. After successfully training machine learning model, experimental studies on basalt fiber reinforced laminates are conducted as validation. Results have proven the effectiveness of the proposed strategy for predicting crack resistance curves, as well as the feasibility of using machine learning-based framework to find out more information about composites from simple experimental data.

The poor ultraviolet (UV) resistance and insufficient solvent compatibility are challenges for long-term storage and service of oil–water separation materials in practical applications. Herein, a superhydrophobic/superoleophilic surface with nano- to microscale hierarchical structures was formed spontaneously on robust microcapsules (MCs) via in situ polymerization and a sol–gel surface treatment. The resultant MCs possessed superior UV-resistant and solvent-proof superhydrophobicity. The water contact angles (WCAs) of the MC coating remained above 160° and the sliding angles (SAs) were below 3° after 9 days of UV aging test or 20 days of nonpolar and polar aprotic solvent immersion tests. More interestingly, these MCs can be used to separate the oil phase from its aqueous emulsion effectively, achieving a high and reusable separation efficiency with over 90% oil purity after 10 cycles of filtrations even after 13 days of UV aging. Therefore, these novel MCs will exhibit effective oil–water separation performance, superior chemical stability, outstanding reusability, and long-term storage stability for promising practical applications.