This paper systematically investigated the topological design of cellular phononic crystals with a maximized gap size between two adjacent bands. Considering that the obtained structures may sustain a certain amount of static loadings, it is desirable to ensure the optimized designs to have a relatively high stiffness. To tackle this issue, we conducted a multiple objective optimization to maximize band gap size and bulk or shear modulus simultaneously with a prescribed volume fraction of solid material so that the resulting structures can be lightweight, as well. In particular, we first conducted the finite element analysis of the phononic band gap crystals and then adapted a very efficient optimization procedure to resolve this problem based on bi-directional evolutionary structure optimization (BESO) algorithm in conjunction with the homogenization method. A number of optimization results for maximizing band gaps with bulk and shear modulus constraints are presented for out-of-plane and in-plane modes. Numerical results showed that the optimized structures are similar to those obtained for composite case, except that additional slim connections are added in the cellular case to support the propagation of shear wave modes and meanwhile to satisfy the prescribed bulk or shear modulus constraints.

This paper contributes to a new design of the three-dimensional printable robotic ball joints capable of creating the controllable stiffness linkage between two robot links through pneumatic actuation. The variable stiffness ball joint consists of a soft pneumatic elastomer actuator, a support platform, an inner ball and a socket. The ball joint structure, including the inner ball and the socket, is three-dimensionally printed using polyamide−12 (PA12) by selective laser sintering (SLS) technology as an integral mechanism without the requirement of assembly. The SLS technology can make the ball joint have the advantages of low weight, simple structure, easy to miniaturize and good MRI compatibility. The support platform is designed as a friction-based braking component to increase the stiffness of the ball joint while withstanding the external loads. The soft pneumatic elastomer actuator is responsible for providing the pushing force for the support platform, thereby modulating the frictional force between the inner ball, the socket and the support platform. The most remarkable feature of the proposed variable stiffness design is that the ball joint has ‘zero’ stiffness when no pressurized air is supplied. In the natural state, the inner ball can be freely rotated and twist inside the socket. The proposed ball joint can be quickly stiffened to lock the current position and orientation of the inner ball relative to the socket when the pressurized air is supplied to the soft pneumatic elastomer actuator. The relationship between the stiffness of the ball joint and the input air pressure is investigated in both rotating and twisting directions. The finite element analysis is conducted to optimize the design of the support platform. The stiffness tests are conducted, demonstrating that a significant stiffness enhancement, up to approximately 508.11 N·mm reaction torque in the rotational direction and 571.93 N·mm reaction torque in the twisting direction at the pressure of 400 kPa, can be obtained. Multiple ball joints can be easily assembled to form a variable stiffness structure, in which each ball joint has a relative position and an independent stiffness. Additionally, the degrees of freedom (DOF) of the ball joint can be readily restricted to build the single-DOF or two-DOFs variable stiffness joints for different robotic applications.

Dielectric elastomer actuators (DEAs), a type of “artificial muscles”, can generate significant deformations and offer speedy responses when exposed to voltage. Owing to their high electromechanical conversion efficiency and great flexibility, they have been extensively used in soft robot applications, such as soft grippers, walking robots, crawling robots, climbing robots, swimming robots, etc. Although previous research has explored the use of DEAs in soft robot locomotion, achieving optimal behavior is challenging due to the complexity of the constituent materials and the highly nonlinear nature of the problem. In this study, a simulation-based design optimization approach is proposed to address this challenge. The proposed approach involves developing a computational modeling framework that evaluates the electromechanical behavior of the DEA. A graph neural network (GNN) is employed as an encoder to extract the latent representation of the geometry in a low dimensional space, which is further used to construct a surrogate model for fast prediction of target responses. To achieve an optimal actuation capability under design constraints, a multi-objective optimization function is formulated to balance the actuation distance and the actuator size, where the Pareto front demonstrates the trade-off between the actuation distance and design constraints. Finally, three optimized designs are fabricated and tested, demonstrating a performance improvement of over 140% compared to an intuitive design. This framework can greatly benefit the design of DEA-based soft robotics.

Recent advancements in soft robotics have seen the rapid development of soft grippers for industrial pick-and-place applications. They are however ill-suited to bear heavy loads due to their compliant nature. Paradoxically, researchers have sought to increase the stiffness of soft grippers to improve loadbearing capabilities. Unfortunately, contemporary soft actuators with variable stiffness are fabricated using manual processes and their performance is subject to an individual's mastery. They are hence not reliable for long-term industrial use. In this paper, we present our work on a 3D-printed metalendoskeleton-reinforced actuator (MERA) for industrial pickand-place applications. We also highlight the fabrication processes needed to recreate it repetitively. Using stainless steel splints (SSS), we demonstrate that MERA is able to modulate its stiffness at selective junctures for stable and effective grasping. We also describe our design rationale with a qualitative mathematical model and validate its performance quantitatively using a finite element model, which is further investigated in the following fatigue test. In our experiments, the MERA equipped with SSS is able to output a peak tip force of 8N, which is a 291% increase compared to the one without metallic reinforcement. In addition, an increase of 76.5% in gripping load and a maximum holding force per actuator of 13.8N are realized through the stiffness tuning of a MERA-Gripper. Despite significantly improving loadbearing capabilities, the actuator manages to retain an overall low profile with a weight of 82g. Finally, we adapted the MERA into a reconfigurable gripper and tested its grasping capabilities on objects of various shapes, sizes, and weights.

This paper introduces a quantum-enhanced finite element method (FEM) designed for noisy intermediate-scale quantum (NISQ) devices, leveraging variational quantum algorithms (VQAs) to solve engineering partial differential equations. We demonstrate the framework by solving the Euler–Bernoulli beam and the NAFEMS T4 heat transfer problems, which involve Dirichlet, Neumann, and Robin boundary conditions. A key innovation is a ‘set-to-zero’ strategy that incorporates boundary conditions through a correction matrix, K bc , allowing for flexible imposition at any node without domain decomposition. The global stiffness matrix is decomposed into a constant number of Pauli terms, O (1), using the method by Sato et al while boundary terms are handled with a sublinearly scaling partial Pauli measurement technique. The algorithm achieves logarithmic qubit scaling ( n = ⌈ log 2 ⁡ N ⌉ qubits for N degrees of freedom(DOF)) and employs shallow, hardware-efficient circuits with empirically trainable depth for small-scale systems. Validation on the Qiskit statevector simulator shows high accuracy. For the Euler–Bernoulli beam problem with 4 to 64 DOF, the algorithm achieves relative errors of 0.5%–1.5% and fidelities of 0.998–0.999. For the NAFEMS T4 heat transfer benchmark, a 5.4% relative error is observed. The VQA converges robustly within 77–350 iterations, though barren plateaus are a known challenge for scaling to larger systems. This work establishes a scalable framework for quantum FEM, offering a significant memory advantage over classical methods and advancing the potential for quantum-enhanced engineering simulations.