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ABSTRACT The discovery of novel thermoset shape memory polymers (TSMPs) for additive manufacturing can be accelerated through the use of a deep‐generative algorithm, minimizing the need for laborious traditional laboratory experiments. This study is the first to introduce an innovative approach that uses a deep generative learning model, namely the conditional variational autoencoder (CVAE), to discover novel TSMPs with lower glass transition temperature () and high recovery stress values (). In this study, specific chemical groups, such as epoxy, amine, thiol, and vinyl, are integrated as constraints to generate novel TSMPs while preserving the essential reaction properties. To address the challenges posed by a small dataset, the CVAE model is used with graph‐extracted features. Unlike previous studies focused on single‐polymer systems, this research extends to two‐monomer samples, discovering 22 novel TSMPs. This approach has practical implications in additive manufacturing, biomedical devices, aerospace, and robotics for the discovery of novel samples from limited data. 

Abstract This study uses the Taguchi optimization methodology to optimize the fatigue performance of short carbon fiber-reinforced polyamide samples printed via fused deposition modeling (FDM). The optimal printing properties that maximize the fatigue limit were determined to be 0.075 mm layer thickness, 0.4 mm infill line distance, 50 mm/s printing speed, and 55 °C chamber temperature with layer thickness being the most critical parameter. To qualify fatigue endurance limit, the energy dissipation in uniaxial fatigue was quantified by using hysteresis energy and temperature rise at steady state. From these results, the fatigue limit for a specimen printed with optimized printing parameters was predicted to be 69 and 70 MPa from hysteresis energy and temperature rise at steady state methods, consecutively, and it was experimentally determined to be 67 MPa. This work demonstrates the effectiveness of the Taguchi optimization method when applied to additive manufacturing and the swift ability to predict the fatigue limit of a material with only one specimen to produce optimal additively manufactured components for industrial applications, as validated by experimental fatigue testing. 

ABSTRACT A trithiol‐triacrylate gel system for frontal polymerization was explored to establish the gelation time, shelf life, and frontal kinetics. The free‐standing gels were created by triethylamine‐catalyzed Michael addition of trimethylolpropane tris(3‐mercaptopropionate) to trimethylolpropane triacrylate such that sufficient acrylate functional groups were left unreacted to allow free‐radical frontal polymerization with the initiator 1,1‐bis(tert‐butylperoxy)‐3,3,5‐trimethylcyclohexane (Luperox 231). Systems with gelation times between 30 and 60 min that support frontal polymerization after up to 28 days of storage were achieved. The front velocity was found to depend on the 1,1‐bis(tert‐butylperoxy)‐3,3,5‐trimethylcyclohexane concentration. However, the amount of triethylamine, which was used to catalyze gel formation, did not significantly affect front velocity. The gel diameter and addition of milled carbon fiber (Zoltek px35) affected the front velocity. Cracks during frontal polymerization were reduced when Zoltek px35 was added to the formulation, which also increased the mechanical strength. Complex geometries of free‐standing gels were successfully polymerized. This system is potentially useful in situations where molding and reshaping gels are required prior to frontal polymerization, as well as enabling the ability to examine how mechanical forces like stretching and compression can affect front kinetics. 

Abstract Frontal polymerization is a process in which a localized reaction zone propagates through the coupling of thermal transport and the Arrhenius kinetics of exothermic polymerization. Most initiators that have been used produce volatile by‐products, which create bubbles and voids. Tetraalkyl ammonium persulfates have been used but these require synthesis and do not have long shelf lives. A charge transfer complex (CTC) composed of an iodonium salt, and a phosphine compound has been identified as a gas‐free initiator for free‐radical thermal frontal polymerization. This CTC has 4‐(dimethylamino)phenyldiphenly phophine (DMAPDP) as the donor and p‐(octyloxyphenyl)phenyliodonium hexafluoroantimonate as the acceptor (IOC‐8). The CTC was tested with several acrylates, and all were found to support bubble‐free fronts. We determined the CTC mole ratio for some monomers at which the front velocity reaches a plateau. 

ABSTRACT Vitrimers with self‐healing, recycling, and remolding capabilities are changing the paradigm for thermoset polymer design. In the past several years, vitrimers that exhibit shape memory effects and are curable by ultraviolet (UV) light have made significant progress in the realm of 4D printing. Herein, we report a molecular dynamics (MD) modeling framework to model UV curable shape memory vitrimers. We used our framework and compared our modeling results with one UV curable shape memory vitrimer found in the literature, bisphenol A glycerolate dimethacrylate. The comparison showed reasonable agreement between the modeling and experimental results in terms of thermomechanical and shape memory properties, along with self‐healing efficiency. It was found that during recycling, it was important for the network to percolate through a majority of the system to get reasonably high recovery stress and recycling efficiency. Once this was achieved, a topological descriptor that was found to represent the compactness of the network was identified as having a very high correlation with recovery stress and recycling efficiency for networks that percolated 70% or more of the monomers in a system. 

Abstract This pioneering study focuses on the finite element analysis (FEA) of thermomechanical properties of shape memory polymer (SMP) wire ropes and their components under both small- and finite-sliding contact deformation. To validate the FEA, we need to validate both geometric modeling and non-linear material behavior. Owing to intricate geometry, as well as excessive wire interactions in the structure, this part is studied by simulating a 1 × 37 steel wire rope and then comparing it with existing experimental data. To evaluate the response of non-linear material behavior, we employ the available numerical results to model the thermomechanical property of an SMP rectangular bar under a uniaxial test and then verify both constrained and unconstrained recovery behavior. After rigorous validation, two configurations of 1 × 7 and 1 × 27 SMP cables are modeled based on the thermo-visco-hyperelastic constitutive framework for acrylate polymer systems. Upon exerting an axially tensile load on these 1 × 7 and 1 × 27 SMP wire ropes, the response of force and shape recovery, as well as the normal and shear stress distributions, are measured under constrained and unconstrained conditions. For a deeper physical understanding, the influences of different temperature rates (5 and 1 °C min⁻¹), inter-wire sliding frictional coefficient (0.1–0.6), and multiple-shape programming on the stress-strain-temperature relations of these SMP cables are also investigated. Furthermore, based on optimizing two cable factors of diameter and helix angle, and using the design of experiments method, the specific energy of a 1 × 6 SMP cable is maximized. Under different thermomechanical loadings, this study tries to cast light on the remarkable features and possible potential applications of these newly developed SMP actuators which may foster unparalleled advancements in various industries. 

Abstract Frontal polymerization (FP) of epoxy monomers is typically achieved with a radical‐induced cationic frontal polymerization (RICFP) process that combines a thermal radical initiator with an onium salt superacid generator. In this paper, we show that both thermal and UV‐initiated cationic frontal polymerizations are possible for common epoxy and vinyl ether monomers with only an iodonium superacid generator in the absence of a standalone thermal radical initiator. Increasing superacid generator concentration resulted in an increase in front velocity, as did the addition of vinyl ether to epoxies. The front velocity is reduced by the addition of 4‐methoxyphenol (MeHQ), indicating free‐radicals play a significant role. 

Abstract Herein, a new lightweight syntactic foam is reported with strong mechanical properties, unique multifunctionalities, and recyclability. Multifunctionality of materials and structures has gained ever‐increasing interest as an excellent approach to designing minimalistic systems. Inspired by nature, these materials can perform multiple functions besides bearing a load. Due to their shape‐changing and damage‐healing property, shape memory vitrimers (SMVs) are a great example of multifunctional materials readily exploited for many applications. Using nickel and silver‐plated hollow glass microbubbles (HGMs), an SMV‐based syntactic foam is introduced here that supplements the multifunctionality of SMVs with electrical conductivity and ferromagnetism, which enables a series of additional potentials such as strain sensing, damage monitoring, Joule heating, and electromagnetic interference shielding. Despite its low density and outstanding mechanical properties, this foam exhibits shape memory behavior, which can be triggered by an electrical current, and damage healing capability due to its reversible dynamic covalent bonds. Especially its recyclability makes recycling the expensive silver‐coated and nickel‐coated HGMs feasible, making this foam cost‐effective and environmentally sustainable. With its many features and economical manufacturability, this syntactic foam has a potential to be utilized in many applications, ranging from aerospace structures to biomedical devices to household items. 

Abstract The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore, it is essential to promote early detection to prevent or alleviate the impact of DR. However, due to the possibility that symptoms may not be noticeable in the early stages of DR, it is difficult for doctors to identify them. Therefore, numerous predictive models based on machine learning (ML) and deep learning (DL) have been developed to determine all stages of DR. However, existing DR classification models cannot classify every DR stage or use a computationally heavy approach. Common metrics such as accuracy, F1 score, precision, recall, and AUC-ROC score are not reliable for assessing DR grading. This is because they do not account for two key factors: the severity of the discrepancy between the assigned and predicted grades and the ordered nature of the DR grading scale.  This research proposes computationally efficient ensemble methods for the classification of DR. These methods leverage pre-trained model weights, reducing training time and resource requirements. In addition, data augmentation techniques are used to address data limitations, improve features, and improve generalization. This combination offers a promising approach for accurate and robust DR grading. In particular, we take advantage of transfer learning using models trained on DR data and employ CLAHE for image enhancement and Gaussian blur for noise reduction. We propose a three-layer classifier that incorporates dropout and ReLU activation. This design aims to minimize overfitting while effectively extracting features and assigning DR grades. We prioritize the Quadratic Weighted Kappa (QWK) metric due to its sensitivity to label discrepancies, which is crucial for an accurate diagnosis of DR. This combined approach achieves state-of-the-art QWK scores (0.901, 0.967 and 0.944) in the Eyepacs, Aptos, and Messidor datasets. 

Abstract We report progress towards development of a cyber-physical trust anchor for additive manufacturing systems. The additive manufacturing commercial sector needs cyber-physical trust anchors to establish a secure supply chain, to detect counterfeiting and to ensure part provenance. However, the underlying technology of cyber-physical trust anchors requires optimization and spans several sectors ranging from mathematics, additive manufacturing, materials science, nondestructive evaluation, to cyber science. The fast and effective deployment of cyber-physical trust anchors requires an educational component. This project present a novel method for authenticating additively manufactured parts. Features are extracted using advanced X-ray imaging, transformed into unique identifiers, and bound with security features for cloud-based blockchain authentication. A plan for the low-cost and safe incorporation of cyber-physical trust anchor research in education is included. The anticipated outcome is an optimized trust anchor prototype and educational product suitable for interdisciplinary research and coursework to develop the workforce needed for cyber-secured physical supply chainsd.