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A sophisticated machine learning framework was developed to design thermally robust shape memory vitrimers (TRSMVs) with superior recycling efficiency, an elevatedT_g, and outstanding shape memory properties, surpassing traditional limitations.

 

Cultivated natural fibers have a huge possibility for green and sustainable reinforcement for polymers, but their limited load-bearing ability and flammability prevent them from wide applications in composites. According to the beam theory, normal stress is the maximum at the outermost layers but zero at the mid-plane under bending (with (non)linear strain distribution). Shear stress is the maximum at the mid-plane but manageable for most polymers. Accordingly, a laminated composite made of hybrid fiber-reinforced shape memory photopolymer was developed, incorporating strong synthetic glass fibers over a weak core of natural hemp fibers. Even with a significant proportion of natural hemp fibers, the mechanical properties of the hybrid composites were close to those reinforced solely with glass fibers. The composites exhibited good shape memory properties, with at least 52% shape fixity ratio and 71% shape recovery ratio, and 24 MPa recovery stress. After 40 s burning, a hybrid composite still maintained 83.53% of its load carrying capacity. Therefore, in addition to largely maintaining the load carrying capacity through the hybrid reinforcement design, the use of shape memory photopolymer endowed a couple of new functionalities to the composites: the plastically deformed laminated composite beam can largely return to its original shape due to the shape memory effect of the polymer matrix, and the flame retardancy of the polymer matrix makes the flammable hemp fiber survive the fire hazard. The findings of this study present exciting prospects for utilizing low-strength and flammable natural fibers in multifunctional load-bearing composites that possess both flame retardancy and shape memory properties.

 

Damage healing in fiber reinforced thermoset polymer composites has been generally divided into intrinsic healing by the polymer itself and extrinsic healing by incorporation of external healing agent. In this study, we propose to use a hybrid extrinsic-intrinsic self-healing strategy to heal delamination in laminated composite induced by low velocity impact. Especially, we propose to use an intrinsic self-healing thermoset vitrimer as an external healing agent, to heal delamination in laminated thermoset polymer composites. To this purpose, we designed and synthesized a new vitrimer, machined it into powders, and strategically sprayed a layer of vitrimer powders at the interface between the laminas during manufacturing. Also, a thermoset shape memory polymer with fire-proof property was used as the matrix. As a result, incorporation of about 3% by volume of vitrimer powders made the laminate exhibit multifunctionalities such as repeated delamination healing, excellent shape memory effect, improved toughness and impact tolerance, and decent fire-proof properties. In particular, the novel vitrimer powder imparted the laminate with first cycle and second cycle delamination healing efficiencies of 98.06% and 85.93%, respectively. The laminate also exhibited high recovery stress of 65.6 MPa. This multifunctional composite laminate has a great potential in various engineering applications, for example, actuators, robotics, deployable structures, and smart fire-proof structures.

 

Abstract In this paper, an open-cell metallic foam was filled in by a tough shape memory polymer (SMP), to form a hybrid metal/polymer composite with multifunctionalities and enhanced mechanical properties. This work aims to study the positive composite actions between the metallic skeleton and the SMP filler. Mechanical, thermal, and conductive properties of the resulting hybrid composite were evaluated and compared to the individual components. Uniaxial compression tests and shape memory effect tests were conducted. Results demonstrated an improvement in the compressive strength and toughness. The hybrid composite also exhibited excellent shape recovery and high recovery stress of 1.76 MPa. Infrared thermography has been used to verify the free shape recovery by Joule heating. Sandwich structures with the hybrid composite as the core were studied through low velocity impact test and three-point bending test. The sandwich structures with the composite foam core showed significant performance improvement in both tests. Electrical resistivity study during the three-point bending test validates the possible application of this multifunctional polymer-aluminum open cell foam composite as strain sensor. This type of hybrid composites can be beneficial in many industrial sectors that search for an ideal combination of high strength, high toughness, low weight, damage sensing, and excellent energy absorption capabilities. 

Cellular materials have been widely used in load carrying lightweight structures. Although lightweight increases natural frequency, low stiffness of cellular structures reduces natural frequency. Designing structures with higher natural frequency can usually avoid resonance. In addition, because of the less amount of materials used in cellular structures, the energy absorption capability usually decreases such as under impact loading. Therefore, designing cellular structures with higher natural frequency and higher energy absorption capability is highly desired. In this study, machine learning and novel inverse design techniques enable to search a huge space of unexplored structural designs. In this study, machine learning regression and Generative Neural Networks (GANs) were used to form an inverse design framework. Optimal cellular unit cells that surpass the performance of biomimetic structures inspired from honeycomb, plant stems and trabecular bone in terms of natural frequency and impact resistance were discovered using machine learning. The discovered optimal cellular unit cells exhibited 30–100% higher natural frequency and 300% higher energy absorption than those of the biomimetic counterparts. The discovered optimal unit cells were validated through experimental and simulation comparisons. The machine learning framework in this study would help in designing load carrying engineering structures with increased natural frequency and enhanced energy absorption capability. 

Laminated multifunctional composites are highly desired in modern lightweight engineering structures. The purpose of this study is to develop a composite laminate with impact tolerance, delamination healing, strain sensing, Joule heating, deicing, and room temperature shape restoration functionalities. In this study, a novel self-healable and recyclable shape memory vitrimer was used as the matrix, unidirectional glass fabric was used as reinforcement, and tension programmed shape memory alloy (SMA) wires were used as z-pins. To provide multifunctionality, the programmed SMA wires were further twisted and formed into sinusoidal shape. Copper wire strands were hooked to the sinusoidal SMA z-pins to make them a closed circuit. Low velocity impact, compression after impact, damage self-healing, deicing, and room temperature shape restoration tests were conducted. The tests result show that the desired multifunctionality of the laminated composite was achieved. The hybrid laminate provides a promising design for lightweight load-carrying engineering structures.