Dynamic topology optimization of 3D-Printed transtibial orthopedic implant using tunable isotropic porous metamaterials.

In this paper, we introduce the design and manufacturing process of a transtibial orthopedic implant. We used medical-grade polyurethane polymer resin to fabricate a 3D porous architected implant with tunable isotropy, employing a high-speed printing method known as Continuous Liquid Interface Production (CLIP). Our objective is to enhance the weight-bearing capabilities of the bone structures in the residual limb, thereby circumventing the traditional reliance on a natural bridge. To achieve a custom-made design, we acquire the topology and morphology of the residual limb as well as the bone structure of the tibia and fibula, utilizing computed tomography (CT) and high-resolution 3D scanning. We employed a dynamic topological optimization method, informed by gait cycle data, to effectively reduce the mass of the implant. This approach, which differs from conventional static methods, enables the quantification of variations in applied forces over time. Using the Euler-Lagrange energy approach, we propose the equations of motion for a homologous multibody model with three degrees of freedom. The versatility of the Solid Isotropic Material with Penalization (SIMP) method facilitates the integration of homogenization methods for microscale porous architectures into the optimized domain. The design of these porous architectures is based on a bias-driven tuning symmetry isotropy of a Triply Periodic Minimal Surface (Schwarz Primitive surface). The internal porosity of the structure significantly reduces weight without compromising the isotropic behavior of the implant.

Flexural Properties of Lattices Fabricated with Planar and Curved Layered Fused Filament Fabrication.

The use of curved layers in fused filament fabrication could lead to various advantages in surface finishing and mechanical properties. Here, the influence of three different structural and manufacturing parameters (volume fraction, raster arrangement, and the use of curved or planar layers) on the mechanical properties of lattice structures under three-point bending is studied. Two different raster arrangements were considered, i.e., those with rasters at planes parallel to the principal axes of the samples and those diagonally arranged, all at four different volume fractions. All different samples were additively manufactured using planar and curved layers. Samples were further dimensionally inspected to refine the computational models before their analysis via finite element simulations. The linear elastic region of the load-displacement curves was further analyzed numerically via finite element models. Predictions with finite element models resulted in good agreement with errors below 10%. Samples with diagonal rasters were 70% softer than those parallel to the principal axes.

Experimental Analysis of Fiber Reinforcement Rings' Effect on Tensile and Flexural Properties of Onyx™-Kevlar® Composites Manufactured by Continuous Fiber Reinforcement.

Additive manufacturing of composite materials is progressing in the world of 3D printing technologies; composite materials allow the combination of the physical and mechanical properties of two or more constituents to create a new material that meets the required properties of several applications. In this research, the impact of adding Kevlar® reinforcement rings on the tensile and flexural properties of the Onyx™ (nylon with carbon fibers) matrix was analyzed. Parameters such as infill type, infill density and fiber volume percentage were controlled to determine the mechanical response in tensile and flexural tests of the additive manufactured composites. The tested composites showed an increment of four times the tensile modulus and 1.4 times the flexural modulus of pure Onyx™ matrix when compared with that of the Onyx™-Kevlar®. The experimental measurements demonstrated that Kevlar® reinforcement rings can increase the tensile and flexural modulus of Onyx™-Kevlar® composites using low fiber volume percentages (lower than 19% in both samples) and 50% of rectangular infill density. However, the appearance of some defects, such as delamination, was observed and should be further analyzed to obtain products that are errorless and can be reliable for real functions as in automotive or aeronautical industries.

Mechanical Properties of AISI 316L Lattice Structures via Laser Powder Bed Fusion as a Function of Unit Cell Features.

The growth of additive manufacturing processes has enabled the production of complex and smart structures. These fabrication techniques have led research efforts to focus on the application of cellular materials, which are known for their thermal and mechanical benefits. Herein, we studied the mechanical behavior of stainless-steel (AISI 316L) lattice structures both experimentally and computationally. The lattice architectures were body-centered cubic, hexagonal vertex centroid, and tetrahedron in two cell sizes and at two different rotation angles. A preliminary computational study assessed the deformation behavior of porous cylindrical samples under compression. After the simulation results, selected samples were manufactured via laser powder bed fusion. The results showed the effects of the pore architecture, unit cell size, and orientation on the reduction in the mechanical properties. The relative densities between 23% and 69% showed a decrease in the bulk material stiffness up to 93%. Furthermore, the different rotation angles resulted in a similar porosity level but different stiffnesses. The simulation analysis and experimental results indicate that the variation in the strut position with respect to the force affected the deformation mechanism. The tetrahedron unit cell showed the smallest variation in the elastic modulus and off-axis displacements due to the cell orientation. This study collected computational and experimental data for tuning the mechanical properties of lattice structures by changing the geometry, size, and orientation of the unit cell.

Additively Manufactured Lattice Materials with a Double Level of Gradation: A Comparison of Their Compressive Properties when Fabricated with Material Extrusion and Vat Photopolymerization Processes.

Natural porous materials adjust their resulting mechanical properties by the optimal use of matter and space. When these are produced synthetically, they are known as mechanical metamaterials. This paper adds degrees of tailoring of mechanical properties by producing double levels of gradation in lattice structures via cross-section variation in struts in uniformly periodic lattice structures (UPLS) and layered lattice structures (LLS). These were then additively manufactured via material extrusion (ME) and vat photopolymerization (VP). Their effective mechanical properties under compressive loads were characterized, and their stiffness contrasted with finite element models (FEM). According to the simulation and experimental results, a better correlation was obtained in the structures manufactured via VP than by ME, denoting that printing defects affect the correlation results. The brittle natural behavior of the resin caused a lack of a plateau region in the stress-strain curves for the UPLS structures, as opposed to those fabricated with ME. The LLS increased energy absorption up to 244% and increased the plateau stress up to 100% compared to the UPLS. The results presented in this paper demonstrate that the mechanical properties of lattice structures with the same base topology could be modified by incorporating variations in the strut diameter and then arranging these differently.

The use of conformal lattice metamaterials for relieving stress in lower limb sockets: A numerical and exploratory study.

Selecting the correct material for each application has always been important. Now, with lattice metamaterials engineers can take advantage of the properties of these metamaterials to best suit a specific application. This paper investigates transtibial lower limb socket stress reduction through the implementation of conformal lattice metamaterials. In this work, a model was obtained with a 3D scanner from a plaster cast taken from a participant with a trans-tibial amputation. Then a 3D socket model was created and two conformal patterns were added to the surface of the socket using nTopology®. Parametric studies to relate the lattice metamaterials constituent elements to their effective structural properties, when such are loaded in-plane and out-of-plane were also included. Pressure test simulations were performed to determine the stresses produced in the sockets. This study concludes with discussion of the results and provides information on how surface conformal patterns can improve socket performance, showing that surface-vertex-centroid patterns increase stiffness and relieve stresses.

Model-Free High-Order Sliding Mode Controller for Station-Keeping of an Autonomous Underwater Vehicle in Manipulation Task: Simulations and Experimental Validation.

The use of autonomous underwater vehicles (AUVs) has expanded in recent years to include inspection, maintenance, and repair missions. For these tasks, the vehicle must maintain its position while inspections or manipulations are performed. Some station-keeping controllers for AUVs can be found in the literature that exhibits robust performance against external disturbances. However, they are either model-based or require an observer to deal with the disturbances. Moreover, most of them have been evaluated only by numerical simulations. In this paper, the feasibility of a model-free high-order sliding mode controller for the station-keeping problem is validated. The proposed controller was evaluated through numerical simulations and experiments in a semi-Olympic swimming pool, introducing external disturbances that remained unknown to the controller. Results have shown robust performance in terms of the root mean square error (RMSE) of the vehicle position. The simulation resulted in the outstanding station-keeping of the BlueROV2 vehicle, as the tracking errors were kept to zero throughout the simulation, even in the presence of strong ocean currents. The experimental results demonstrated the robustness of the controller, which was able to maintain the RMSE in the range of 1-4 cm for the depth of the vehicle, outperforming related work, even when the disturbance was large enough to produce thruster saturation.

Towards the Development of 3D-Printed Food: A Rheological and Mechanical Approach.

Additive manufacturing, or 3D printing, has raised interest in many areas, such as the food industry. In food, 3D printing can be used to personalize nutrition and customize the sensorial characteristics of the final product. The rheological properties of the material are the main parameters that impact the 3D-printing process and are crucial to assuring the printability of formulations, although a clear relationship between these properties and printability has not been studied in depth. In addition, an understanding of the mechanical properties of 3D-printed food is crucial for consumer satisfaction, as they are related to the texture of food products. In 3D-printing technologies, each manufacturing parameter has an impact on the resulting mechanical properties; therefore, a thorough characterization of these parameters is necessary prior to the consumption of any 3D-printed food. This review focuses on the rheological and mechanical properties of printed food materials by exploring cutting-edge research working towards developing printed food for personalized nutrition.

Effective Stiffness of Fused Deposition Modeling Infill Lattice Patterns Made of PLA-Wood Material.

Fused deposition modeling (FDM) uses lattice arrangements, known as infill, within the fabricated part. The mechanical properties of parts fabricated via FDM are dependent on these infill patterns, which make their study of great relevance. One of the advantages of FDM is the wide range of materials that can be employed using this technology. Among these, polylactic acid (PLA)-wood has been recently gaining attention as it has become commercially available. In this work, the stiffness of two different lattice structures fabricated from PLA-wood material using FDM are studied: hexagonal and star. Rectangular samples with four different infill densities made of PLA-wood material were fabricated via FDM. Samples were subjected to 3-point bending to characterize the effective stiffness and their sensitivity to shear deformation. Lattice beams proved to be more sensitive to shear deformations, as including the contribution of shear in the apparent stiffness of these arrangements leads to more accurate results. This was evaluated by comparing the effective Young's modulus characterized from 3-point bending using equations with and without shear inclusion. A longer separation between supports yielded closer results between both models (~41% for the longest separation tested). The effective stiffness as a function of the infill density of both topologies showed similar trends. However, the maximum difference obtained at low densities was the hexagonal topology that was ~60% stiffer, while the lowest difference was obtained at higher densities (star topology being stiffer by ~20%). Results for stiffness of PLA-wood samples were scattered. This was attributed to the defects at the lattice element level inherent to the material employed in this study, confirmed via micro-characterization.

Experimental Validation of a Model-Free High-Order Sliding Mode Controller with Finite-Time Convergence for Trajectory Tracking of Autonomous Underwater Vehicles.

Several control strategies have been proposed for the trajectory tracking problem of Autonomous Underwater Vehicles (AUV). Most of them are model-based, hence, detailed knowledge of the parameters of the robot is needed. Few works consider a finite-time convergence in their controllers, which offers strong robustness and fast convergence compared with asymptotic or exponential solutions. Those finite-time controllers do not permit the users to predefine the convergence time, which can be useful for a more efficient use of the robot's energy. This paper presents the experimental validation of a model-free high-order Sliding Mode Controller (SMC) with finite-time convergence in a predefined time. The convergence time is introduced by the simple change of a time-base parameter. The aim is to validate the controller so it can be implemented for cooperative missions where the communication is limited or null. Results showed that the proposed controller can drive the robot to the desired depth and heading trajectories in the predefined time for all the cases, reducing the error by up to 75% and 41% when compared with a PID and the same SMC with asymptotic convergence. The energy consumption was reduced 35% and 50% when compared with those same controllers.

Sensor Data Fusion for a Mobile Robot Using Neural Networks.

Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D 360° LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visible to traditional 2D LiDAR sensors, hence the expression improved LiDAR. A preprocessing scheme is implemented to filter all the outliers, project a 3D pointcloud to a 2D plane and adjust distance data. With a Neural Network as a data fusion algorithm, we integrate all the information into a single, more accurate distance-to-obstacle reading to finally generate a 2D Occupancy Grid Map (OGM) that considers all sensors information. The Robotis Turtlebot3 Waffle Pi robot is used as the experimental platform to conduct experiments given the different fusion strategies. Test results show that with such a fusion algorithm, it is possible to detect glass and other obstacles with an estimated root-mean-square error (RMSE) of 3 cm with multiple fusion strategies.

Adaptable Bed for Curved-Layered Fused Deposition Modeling of Nonplanar Structures: A Proof of Concept.

Curved-layered fused deposition modeling demands a curved mandrel for each of the designs to fabricate. This results in more fabrication time and material used. To overcome this, an adaptable pin-base mandrel is presented. The fabrication of curved structures with such mandrel uses the extruder as a pushing tool, pushing each of the pins to the depths required for each surface. After the base surface is formed, the conventional process of curved fused deposition modeling (FDM) is used. Several lattice shell structures composed of nonplanar layers were fabricated on FDM mandrels and the adaptable pin-bed proposed here. The comparison of the manufacturing results showed that the adaptable base allows the successful fabrication of the samples. The solution exposed here represents a proof of concept to validate the idea. The adaptable base presented in this study is unique and it brings advantages to the fabrication of curved-layered structures.

Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators.

New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83° was achieved when validated against several set-points within the possible range.

Characterization of the Mechanical Properties of FFF Structures and Materials: A Review on the Experimental, Computational and Theoretical Approaches.

The increase in accessibility of fused filament fabrication (FFF) machines has inspired the scientific community to work towards the understanding of the structural performance of components fabricated with this technology. Numerous attempts to characterize and to estimate the mechanical properties of structures fabricated with FFF have been reported in the literature. Experimental characterization of printed components has been reported extensively. However, few attempts have been made to predict properties of printed structures with computational models, and a lot less work with analytical approximations. As a result, a thorough review of reported experimental characterization and predictive models is presented with the aim of summarizing applicability and limitations of those approaches. Finally, recommendations on practices for characterizing printed materials are given and areas that deserve further research are proposed.

Ex-vivo models of the Retinal Pigment Epithelium (RPE) in long-term culture faithfully recapitulate key structural and physiological features of native RPE.

The Retinal Pigment Epithelium (RPE) forms the primary site of pathology in several blinding retinopathies. RPE cultures are being continuously refined so that dynamic disease processes in this important monolayer can be faithfully studied outside the eye over longer periods. The RPE substrate, which mimics the supportive Bruch's membrane (BrM), plays a key role in determining how well in-vitro cultures recapitulate native RPE cells. Here, we evaluate how two different types of BrM substrates; (1) a commercially-available polyester transwell membrane, and (2) a novel electrospun scaffold developed in our laboratory, could support the generation of realistic RPE tissues in culture. Our findings reveal that both substrates were capable of supporting long-lasting RPE monolayers with structural and functional specialisations of in-situ RPE cells. These cultures were used to study autofluorescence and barrier formation, as well as activities such as outer-segment internalisation/trafficking and directional secretion of key proteins; the impairment of which underlies retinal disease. Hence, both substrates fulfilled important criteria for generating authentic in-vitro cultures and act as powerful tools to study RPE pathophysiology. However, RPE grown on electrospun scaffolds may be better suited to studying complex RPE-BrM interactions such as the formation of drusen-like deposits associated with early retinal disease.