Soft robotics requires structural systems capable of performing complex and programmable deformations to adapt to unstructured or dynamic environments. Shape memory materials (SMMs) offer a promising solution owing to their shape memory effect and stimulus-responsive adaptability. However, actuators relying on a single type of SMM are often constrained by nonlinear actuation behavior and limited stiffness variation, which restrict their ability to achieve coordinated, multifunctional responses. Addressing these challenges, this study introduces a hybrid programmable morphing structure that integrates a shape memory polymer (SMP) and a shape memory alloy (SMA) to realize cooperative actuation and adaptive stiffness variation within a single unit. In the proposed configuration, the SMA springs act as thermally activated actuators that generate deformation. The SMP cylindrical core employs its shape memory effect to realize reversible shape locking and serves as a thermal switch that enables controlled stiffness variation through temperature regulation. A coupled numerical model was established to describe the cooperative behavior between the SMA and SMP components, and the numerical results were validated through experimental testing. The agreement between simulations and experiments confirms the feasibility and repeatability of the proposed design. The structure achieves a maximum bending angle of 55° under dual-SMA actuation and 42° under single-SMA actuation, while maintaining any intermediate shape during thermal cycling. Furthermore, the hybrid system demonstrates a reversible six-fold increase in stiffness and a motion range extending up to three times its original length, representing a significant improvement over conventional single-material soft actuator. Moreover, the proposed hybrid structure offers a flexible strategy for programmable morphing and demonstrates scalable applicability in practical applications, such as adaptive grasping, reconfigurable locomotion, and environmental exploration. In conclusion, this work provides a feasible and generalizable framework for integrating multiple SMM into programmable morphing structures which can be applied into multifunctional soft robotic systems.

Neuroendoscopy treats intracranial pathologies through millimeter-scale channels using endoscopes introduced along a straight trajectory from a cranial entry point to the target. The entry point acts as a Remote Center of Motion (RCM), which must remain fixed to follow the surgical plan and avoid damage around the entry point. Existing robotic RCM platforms rely on rigid multi-link structures, increasing complexity and footprint. To mitigate these limitations, we propose a compact dual-joint compliant mechanism for neuroendoscopic manipulation. Building on the Tetra II flexure architecture, we redesigned and optimized the joint for neurosurgical use. The end-effector holder is moved from the central axis to the side to improve visual access, facilitate sterile draping and allow rapid instrument exchange while preserving the RCM constraint. The mechanical design targets directionally uniform stiffness in the working plane while minimizing parasitic RCM displacements. The mechanism uses two identical compliant joints in series, with the connection angle treated as a design variable. For each angle, the response is obtained by analyzing each joint separately in FEM and combining their contributions via rotation matrices. An angular offset of 300° yields near-isotropic stiffness, with a root-mean-square error of 0.90 N/m from an ideal isotropic behavior. A PA12 prototype was tested under 0.1±0.01 N radial loads. Experimental stiffness differed by ≤19% from FEM. The parasitic RCM displacement was 0.032 ± 0.018 mm for a 4.5°shaft rotation, well within the 1 mm neurosurgical tolerance. This dual-joint compliant RCM mechanism offers a practical alternative to conventional rigid-link designs.

Fused filament fabrication is a popular extrusion 3D printing technology because of its affordability and accessibility. However, the approach often suffers from printing errors that result in wasted time, materials and energy. Convolutional neural networks can be trained to recognise a wide spectrum of printing anomalies from image data in real time, but past work has been limited to a few defect classifications at a time. Here, we introduce a fault detection system, designed to identify a range of errors without interrupting the printing process. Real-time detection is achieved using a pre-trained image recognition and pattern recognition convolutional neural network (CNN) with two mounted cameras on the print bed and a nozzle camera. Two CNN models are developed to classify images into common 3D printing errors for the two camera systems. The nozzle camera model achieves a high validation accuracy of 97.7%. The side camera model achieves comparable performance with a validation accuracy of 97.6%. To integrate the two CNNs into one unified system, a logic-based priority framework was used to improve reliability beyond individual model accuracies by resolving conflicting predictions and leveraging complementary viewing angles from both camera types to detect a broader range of defects. The data fusion framework identifies 12 common errors and has significantly improved the robustness of error classification, in-situ and in real-time, with inference times as small as 220 milliseconds. The results demonstrate the feasibility of a robust multi-input fault detection system to advance the reliability of extrusion 3D printing.

This study introduces a computational framework for modelling raw chicken breast fillets using the Discrete Element Method (DEM), aimed at providing a baseline efficient simulation model for large-scale poultry handling processes. A bonded multi-sphere meta-particle representation was developed and calibrated through mechanical testing of raw fillets. Compression experiments yielded a Young’s modulus of approximately 48.6 kPa, which informed the stiffness properties of the DEM sub-particle assembly. Numerical Design of Experiments (DoEs) highlighted the need for an unbalanced ratio between normal and shear bond stiffness to ensure correct damping behaviour and preserve realistic flexibility. The framework was validated using a full-scale hopper–conveyor discharge experiment, demonstrating the model’s ability to reproduce key physical behaviours such as large deformations, curling during discharge, and the transition between jammed and free-flow regimes. The simulation closely matched the measured discharge rate, with all chicken fillets discharged within 4 s at a 6 cm gate opening height. The proposed model required approximately 9 mins to simulate a 10-second industrial-scale process, underscoring the model’s practical suitability for simulation-aided design and optimisation of poultry processing equipment.

Mechanical metamaterials are architected structures with unique functionalities, such as negative Poisson's ratio and negative stiffness, which are widely employed for absorbing energy of quasi-static and impact loads, giving improved mechanical response. Acoustic/elastic metamaterials, their dynamic counterparts, rely on frequency-dependent properties of their microstructure elements, including mass density and bulk modulus, to control the propagation of waves. Although such metamaterials introduced significant contribution for solving independently static and dynamic problems, they were facing certain resistance to their use in real-world engineering problems, mainly because of a lack of integrated systems possessing both mechanical and vibration attenuation performance. Advances in manufacturing processes and material and computational science now enable the creation of hybrid mechanical metamaterials, offering multifunctionality in terms of simultaneous static and dynamic properties, giving them the ability of controlling waves while withstanding the applied loading conditions. Exploring towards this direction, this review paper introduces the hybrid mechanical metamaterials in terms of their design process and multifunctional properties. We emphasize the still remaining challenges and how they can be potentially implemented as engineering solutions.

The importance of natural environments with rugged deformable terrain from biodiversity, carbon capture, and coastal protection to economic livelihood is significant. However, the current systems available for robots to explore those ecosystems are either large, expensive and intrusive, not application focused or consist of many mechanical parts prone to failure. This study proposes a soft adaptable wheel designed and verified using a novel modelling-based approach suited for such ecosystems. The novel modelling techniques used a 3 part iterative design framework including a kinematic analysis using multi-body dynamics, structural feasibility tests using the finite element method and deformable terrain testing using the discrete element method. The final design operates as a soft fluidic actuator constructed with silicone, able to change its form depending on the task at hand. The proposed model is intended to be a more application-driven design (for rugged deformable terrain), that can more easily be integrated into robotic systems using off-the-shelf components. The simplicity and symmetry of the model can be easily scaled according to the terrain type, load requirements or application of the robotic system, ultimately reducing the time required to be used in environmental applications.

The challenge of designing real-world robots continues due to the complexities of navigating inaccessible terrains and encountering unexpected conditions. Introducing smart materials like shape memory alloys (SMAs) in the robot body can be beneficial due to their shape memory effect for actuation; however, there is no systematic way to introduce SMAs in a robot design. This research aims to address these challenges by proposing a design framework for SMA-actuated smart structures in robotic applications. Drawing inspiration from nature, the initial step in this framework involves conceptualizing a multifunctional grasper. This grasper utilizes SMA springs actuated by electric current, enabling various movements such as crawling, grasping, and folding. Analytical modeling is employed to determine the necessary characteristics of the SMA springs for one segment of the grasper. A multi-body modeling approach is utilized for more comprehensive understanding of the robot performance. This approach verifies the results of the analytical modeling and allows for performance optimization. Grasper’s dynamics is enhanced by fine-tuning actuation input signals, resulting in a more precise, sustainable, and energy-efficient grasper that is capable of traveling 400% longer distance than the initial concept design. The conducted experiments confirm that the proposed design framework for mechanically intelligent grasper has the potential to streamline the SMA-actuated structure design process by reducing development time, minimizing the trial-and-error iterations, and yielding cost savings in both development and prototyping phases.

Locomotion over granular terrain poses significant challenges for autonomous robotic systems, particularly in coastal regions characterized by loose, shifting sands. To optimize the locomotion on these challenging terrains, a simulation-aided design approach was used to develop a soft, shape-adapting, wheeled locomotion system. A co-simulation framework combining the discrete element method (DEM) and multibody dynamics (MBD) is employed to simulate the locomotion of a wheeled robot on varying sandy soils, covering both dry and wet sandy soil conditions. A shape-adapting wheel design is proposed, incorporating soft, inflatable elements that enable the wheel to transform between lugged and circular configurations. A discretized flexbody approach is adopted to model the interactions between the sandy soil and the soft, flexible bodies of the shape-adapting wheel design. Simulation results demonstrate improved performance of the shape-adapting wheels across a variety of sandy terrains, including slopes and obstacles. Integrating softness into the wheel improves obstacle climbing performance, while a lugged wheel configuration performs particularly well on loose, dry sandy slopes. This DEM-MBD co-simulation further enables efficient evaluation of locomotion strategies without the need for extensive physical prototyping.

Enceladus, one of Saturn’s icy moons, has been a subject of intense scientific interest since the Cassini mission revealed a subsurface ocean containing salts and complex organic molecules. This ocean, buried beneath kilometers of ice, is accessible only through surface cracks at the moon’s south pole, where geysers emerge. In support of future missions searching for extraterrestrial life within our solar system, we developed a robot aimed at exploring such environments. Using Peltier elements, the robot attaches to icy surfaces by locally melting and refreezing water and detaches by re-melting the contact area. Adhesion tests based on local phase change dynamics demonstrate strong bonding, often exceeding the cohesive strength of the ice. While originally developed for planetary exploration, the underlying principle is also applicable to Earth-based operations such as exploration and rescue missions in icy environments.

Enceladus, one of Saturn’s moons, is considered one of the most promising places in the solar system to find life. The Cassini mission discovered organic-rich water plumes from Enceladus’s subsurface ocean, prompting new lander mission planning. We developed a mechanically simple ice sampling system for autonomous life detection on lander missions. The system is controlled by a single rotary actuator that samples, liquefies, and prepares ice for microscopic observations. Sample acquisition uses a novel conical boundary layer pump that delivers samples to a microfluidic disk. A digital holographic microscope detects microorganisms without mechanical focusing. The single-actuator design enables closed-loop control of velocity, position, and torque, with an operational sequence controlling fluid dynamics in a centrifugal microfluidic disk. Testing demonstrated system feasibility and effectiveness across all subsystems. Open-source software was developed for automated onboard hologram processing, including organism motility detection to assess presence of life. This single-actuator design reduces mechanical complexity for lander missions. Future work focuses on adapting the technology for terrestrial applications and achieving required technology readiness levels for space deployment.

The growth of offshore wind farms is accelerating to meet the renewable energy target by 2030, driving the development of larger offshore wind turbines (OWTs) to boost energy capacity. To support these OWTs, large monopiles are being installed by using impact hammers, which in turn emit low-frequency underwater noise, posing challenges for traditional noise mitigation systems and increasing risks to marine life. To address this, a metamaterial-based cushion (meta-cushion) was proposed, embedding resonators to filter longitudinal waves associated with high underwater noise levels. While prior work has demonstrated the meta-cushion's noise attenuation potential, design guidelines are required for adaptation to various monopile installations. This paper introduces, for the first time, a design methodology for the meta-cushion, which based on the input parameters of the monopile system, it details the procedure for selecting the resonant elements contributing to the attenuation performance and their spatial arrangement on the cushion for enhancing mechanical performance. Such performance indicators are evaluated via finite element simulations and experimental modal analyses. The methodology concludes with a nondimensional study of the spiral resonator, which showed the best attenuation response in experiments, exploring its behavior under varying material and geometric parameters. This methodology enables the development of meta-cushions adaptable to monopile installations under any environmental conditions.

Monopiles are the dominant foundation type for offshore wind turbines, accounting for approximately 80% of the installed capacity. Installing offshore monopile foundations on seabeds susceptible to scour erosion requires monopiles to penetrate several pre-installed scour protection rock layers before securing them into the seabed. The accurate prediction of the pile penetration resistance is crucial to ensure successful monopile installations. To complement, and potentially reduce the dependence on the costly and labour-intensive experimental small-scale penetration tests, a numerical model has been developed using the Discrete Element Method (DEM) that captures the discrete nature of interactions between rocks and piles and predicts the resistance during the penetration process. The developed DEM model includes armour and filter rocks represented by multispheres and sand particles represented by spheres. A multistage calibration, verification and validation DEM modelling framework is proposed and examined with small-scale penetration tests conducted using plates and piles in a double-layer scour protection configuration. The sand material model is calibrated and verified using penetrometer tests and the rock material models are calibrated and verified using a plate penetration test. The DEM model with three verified materials predicts the penetration resistance well in small-scale pile penetration tests and proves the validity of the proposed framework. The DEM model presented in this paper facilitates the modelling in areas where traditional continuum-based numerical methods give less accurate predictions and provide insights that are difficult or nearly impossible to obtain through experimental methods.