The design and engineering of liquid crystal elastomers (LCE) composites for enhanced multifunctionality and responsiveness is highly desired. Here, a hollow LCE (h‐LCE) fiber fabricated via coaxial spinning, enabling the straightforward yet effective creation of functional LCE composites, is reported. Inspired by the fiber‐tubule architecture in skeletal muscles, the hollow fiber features an LCE outer shell for programmable actuation and an inner channel allowing for the integration of a variety of functional media. Thus, the h‐LCE fiber can serve as a versatile platform for multifunctionalities in LCE composites. With this unique design strategy, h‐LCE fibers are fabricated with lengths exceeding 3 meters in the lab with outer and inner diameters as small as 250 mm and 120 µm, respectively. The versatility of these h‐LCE fibers across various applications are further demonstrated, from fast‐response stiffness‐tunable actuators by integrating water flow as triggering media and shape memory polymer (SMP) for enhanced mechanical properties, to electrically driven actuating systems through the incorporation of liquid metal, and actuating light‐guides by combining SMP and PDMS optical fiber. The conception of h‐LCE fiber not only advances the design of multifunctional LCE composites but also paves the way for their application in soft robotics, artificial muscles, and beyond.

This paper presents a novel computational design and fabrication method for fabric-based soft pneumatic actuators (FSPAs) that use Turing patterns, inspired by Alan Turing’s morphogenesis theory. These inflatable structures can adapt their shapes with simple pressure changes and are applicable in areas like soft robotics, airbags, and temporary shelters. Traditionally, the design of such structures relies on isotropic materials and the designer’s expertise, often requiring a trial-and-error approach. The present study introduces a method to automate this process using advanced numerical optimization to design and manufacture fabric-based inflatable structures with programmable shape-morphing capabilities. Initially, an optimized distribution of the material orientation field on the surface membrane is achieved through gradient-based orientation optimization. This involves a comprehensive physical deployment simulation using the nonlinear shell finite element method, which is integrated into the inner loop of the optimization algorithm. This continuous adjustment of material orientations enhances the design objectives. These material orientation fields are transformed into discretized texture patterns that replicate the same anisotropic deformations. Anisotropic reaction-diffusion equations, using diffusion coefficients determined by local orientations from the optimization step, are then utilized to create space-filling Turing pattern textures. Furthermore, the fabrication methods of these optimized Turing pattern textures are explored using fabrics through heat bonding and embroidery. The performance of the fabricated FSPAs is evaluated through three different deformation shapes: C-shaped bending, S-shaped bending, and twisting.

Its high-specific strength and stiffness with lower cost make discontinuous fiber-reinforced thermoplastic (FRT) materials an ideal choice for lightweight applications in the automotive industry. Compression molding is one of the preferred manufacturing processes for such materials as it offers the opportunity to maintain a longer fiber length and higher volume production. In the past, we have demonstrated that compression molding of FRT in bulk form can be simulated by treating melt flow as a continuum using the conservation of mass and momentum equations. However, the compression molding of such materials in sheet form using a similar approach does not work well. The assumption of melt flow as a continuum does not hold for such deformation processes. To address this challenge, we have developed a novel simulation approach. First, the draping of the sheet was simulated as a structural deformation using the explicit finite element approach. Next, the draped shape was compressed using fluid mechanics equations. The proposed method was verified by building a physical part and comparing the predicted fiber orientation and warpage measurements performed on the physical parts. The developed method and tools are expected to help in expediting the development of FRT parts, which will help achieve lightweight targets in the automotive industry.

In this paper, we present a novel multilayered morphing structure, having similar topology resembling the structures found in nature to grasp delicate objects effectively as well as sense contact force and temperature. The structure consists of two actuation layers, two U-shaped cooling channel layers, piezoelectric based touch sensors and temperature sensors. Employing shape memory alloy (SMA) spring actuators for bending and twisted and coiled polymer fishing line with nichrome (TCP_FL ^NMC) artificial muscles for antagonistic return, the soft silicone-based composite skin exhibits unique capabilities of large bidirectional movement, avoid rigid passive springs for return motion, soft grasping, safe interaction with humans, and ease fabrication. The SMA (0.38 mm wire diameter) serves as relatively fast actuating muscle and the TCP_FL ^NMC (0.8 mm fiber diameter) as a slow actuating (considering mainly heating cycle), which was programmed/designed to mimic the fast and slow twitching muscles found in nature. Bending and return operations of skin samples of length 100 mm and thickness of 9 mm, with three different widths 20 mm, 25 mm, 30 mm, were experimentally studied. The 25 mm wide multilayered soft skin demonstrated cyclic actuation with a maximum bending angle of ∼70°, which was attributed due to the active cooling. The fluidic channels for active cooling were fabricated using 3D printed PVA tubes, casting within the silicone in a mold and subsequently dissolving in a circulating water. The study also included the integration and voltage response of mini-piezodisk sensor PIC255 having a diameter of 2 mm and thickness of 0.15 mm, which was embedded at different depths within the silicone (on the surface, 1 mm depth and 2 mm depth). The multilayered soft skin was also able to detect the temperature of the object during grasping, suggesting its potential application as a soft gripper in robotic systems.