The design freedom of additive manufacturing has the potential to increase the complexity, functionality, and mechanical behavior of compliant mechanisms compared to conventional manufacturing methods. However, this same design freedom can result in “trial-and-error” design iteration when designing compliant mechanisms for target behaviors. Trial-and-error design iteration is undesirable and inefficient, particularly when designing compliant mechanisms with nonlinear-elastic materials and large deformations. To address this problem, we develop a synthesis method for the inverse design of compliant mechanisms that uses numerical optimization and a library of building blocks that incorporate nonlinear-elastic materials and large deformations as geometric nonlinearity. The synthesis method works by selecting the type and geometry of the building blocks that will deflect to a target shape under a given load, or achieve a target stiffness response. The method's ability to use different building block geometries and incorporate nonlinear behaviors, such as the superelasticity of shape memory alloys, is shown to enhance the design of compliant mechanisms to reach target shapes or stiffness responses.

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.

The increasing installation of large offshore monopiles for wind turbine foundations generates harmful underwater noise during pile driving, particularly at low frequencies. Existing noise mitigation systems target noise around 1000 Hz, however, their performance reduces for lower frequency noise expected for the upcoming monopiles. To address this, this paper develops and investigates a new noise mitigation system – a metamaterial-based curtain, or meta-curtain-using metamaterials and smart materials to filter sound waves generated during the pile installation, focusing mainly on the primary noise source – from pile to water. The meta-curtain is developed via a design methodology that covers the understanding of bubble fundaments, the attenuation performance in the pile-water system, and the structural integrity and deployment mechanism of the curtain frame. This paper focuses mainly on the steps related to the acoustic of the new bubble designs and their attenuation performance. Future work explores the structural performance of the meta-curtain. This work opens the door to the investigation of new designs that can potentially reduce the propagation of low-frequency waves from monopile installations.

McKibben Artificial Muscles (AMs) are widely used in soft robotics as actuators due to their compliance and high force output. However, achieving complex motions with multiple AMs in different orientations remains challenging, as each requires a bulky pneumatic or hydraulic system, complicating assembly and increasing the risk of leaks. These limitations restrict their use in compact or lightweight applications. Inspired by natural muscle architectures that optimize force distribution and enable multidirectional motion, this study aims to integrate complex fiber arrangements into a single McKibben AM. A novel fabrication technique was developed using an interplay between multiple traditional Japanese Kumihimo braiding disks to create multi-directional AMs. Two case studies demonstrate the approach: (1) a bending actuator capable of achieving programed bending angles depending on actuation pressure and (2) a convergent actuator whose combined branches reconfigured output force direction to extend motion range. The resulting branched AMs locally encode complex motion within their structure, eliminating the need for complex actuator systems. This manufacturing strategy advances McKibben AM design toward more compact, adaptive and versatile systems for advanced soft robotic applications.

This paper presents an off-axis, monolithic compliant Remote Center of Motion (RCM) joint for neuroendoscopic manipulation, combining near-isotropic stiffness with minimal parasitic motion. Based on the Tetra II concept, the end-effector is placed outside the tetrahedral flexure to improve line of sight, facilitate sterilization, and allow rapid tool release. Design proceeds in two stages: mobility panels are sized with a compliance-based isotropy objective, then constraining panels are synthesized through finite-element feasibility exploration to trade stiffness isotropy against RCM drift. The joint is modeled with beam elements and validated via detailed finite-element analyses, including fatigue-bounded stress constraints. A PA12 prototype is fabricated by selective laser sintering and characterized on a benchtop: a 2 N radial load is applied at the end-effector while a 6-DOF electromagnetic sensor records pose. The selected configuration produces a stiffness-ellipse principal axis ratio (PAR) of 1.37 and a parasitic-to-useful rotation ratio (PRR) of 0.63%. Under a 4.5° commanded rotation, the predicted RCM drift remains sub-millimetric (0.015-0.172 mm). Fatigue analysis predicts a usable rotational workspace of 12.1°-34.4° depending on direction. Experiments reproduce the simulated directional stiffness trend with typical deviations of 6-30%, demonstrating a compact, fabrication-ready RCM module for constrained surgical access.

Origami-inspired design and additive manufacturing enable the rapid fabrication of compact parallel mechanisms with integrated compliance and reduced assembly complexity. This article presents OriDelta, an origami-inspired Delta robot (137 × 158 × 84 mm) fabricated via multimaterial extrusion additive manufacturing with compliant flexural joints. Experimental validation using optical motion capture revealed a systematic, configuration-dependent platform tilt arising from the interaction between mechanism geometry and flexure joint compliance. This parasitic motion exhibits a strong linear correlation with platform position and directly contributes to the positioning error. Exploiting this predictability, we develop a two-stage feedforward compensation model to predict tilt from commanded positions and correct the resulting position error. The proposed approach reduced the rms positioning error to 1.21 ± 0.53 mm, corresponding to an average improvement of 50.2% in kinematic accuracy across all trajectories and protocols. These results demonstrate the effectiveness of model-based compensation in compliant origami mechanisms and provide quantitative insight into the role of compliance-induced parasitic motion in origami-inspired parallel manipulators.

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.

Shape memory alloys (SMAs) or shape memory polymers (SMPs) enable soft actuators to achieve advanced adaptabilities applied in soft robotics. However, actuators that combine multiple shape memory materials struggle to achieve complex deformation effects and stiffness variations with effective control strategies. To achieve controllable, shape adaptation, and programmed deforming behavior, this study proposes an integrated control strategy for an SMA–SMP based programmable morphing structure used as an actuator in soft robotics. To achieve precise control over programming deformations and stiffness variation, a multi-target thermal sensing method (MTTSM) was proposed, integrated into an interaction-driven control framework. Based on MTTSM, the coordinated actuation between the SMA springs and the SMP structure is realized, enabling standby of preheating, stepping with programmed deformations, and dynamic stiffness changes. In addition, to achieve dynamic monitoring of deformed states, the co-training-based monitoring system is developed for collaboration, enabling the use of multisensor fusion for position estimation in the absence of end-effectors that can directly measure the deformed structure of the flexible body. In conclusion, the proposed integration strategy of MTTSM and the cotraining monitoring system offers a control solution for integrating multiple shape memory materials into morphing structures as smart actuators applied to soft robotic applications.

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.

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.

The use of optimization procedures for designing acoustic/elastic metamaterials (A/E MMs) has gained significant interest since they enable the efficient attainment of unique functionalities often contradicting. When it comes to vibration attenuation caused by mechanical stress waves, such as impact loads, the dynamic properties of A/E MMs are optimized so that their wave-control ability is maximized. However, the mechanical performance of A/E MMs during the propagation of such waves is normally not evaluated into the design optimization stages. This may compromise not only the load-bearing capacity of MMs, but also their ability in attenuating vibrations. To prevent such effects, we propose a design strategy that incorporates the stress analysis in the early design phase of A/E MMs subjected to an impact load. The effective mass density approach is applied, from which the vibration attenuation is identified at frequency ranges where the resonator moves out-of-phase in relation to the applied excitation. Regarding to the A/E MM mechanical behavior, maximum von Mises stress is calculated through the transient analysis of a unit cell array subjected to a dynamic load. A Pareto front shows a trade-off behavior between the A/E MM functionalities. With that, we emphasize the importance of incorporating the mechanical performance into the design stage of A/E MMs for vibration attenuation of structures undergoing high impact loads, such as installation of foundations by impact hammering. This brings A/E MMs closer to real applications involving energy filtering at specific frequencies from transient loads, designed in an optimized and efficient way.

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.

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.

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.

Hyper-redundant manipulators offer high dexterity and manoeuvrability in constrained environments, yet their design must integrate structural efficiency with environmental adaptability. This study presents a co-design framework for lightweight, cable-driven hyper-redundant manipulators optimised for underwater applications, such as the usage in combination with a Remotely Operated Vehicle. Building on a modular architecture of an eight-degree-of-freedom cable-driven manipulator, the methodology integrates Gaussian process regression-based stress prediction and generative design to achieve mass and size reductions, as well as a hydrodynamically efficient shape while ensuring structural integrity under extreme static loads. A 3D-printed module fabricated from Onyx, a carbon fibre-reinforced nylon, achieved near-neutral buoyancy in seawater, validated through submerged testing of a two-module prototype. An external buoyant element design was then provided for manipulators with no inherent buoyancy, accounting for printability, density mismatch between actual and theoretical density, and joint range of motion. This work advances underwater hyper-redundant robot design by combining data-driven optimisation with modular buoyancy strategies and hydrodynamic efficiency, providing a scalable method for fluid environments.