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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.

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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.