Mechanical metamaterials are materials that appear to have unusual elastic mechanical properties, with the most prominent being a negative Poisson’s ratio. The key to their unique properties lies in the microarchitecture of the material rather than the material itself. For advanced applications in the realm of prosthetics, such as form matching, aperiodic metamaterials have been recommended above repetitive ones. In a restricted lattice, metamaterials with disordered microarchitecture have displayed a wide variety of anisotropic conventional and auxetic behaviour. Because of the non-unique and non-intuitive link between the random network (RN) structures and their mechanical responses, their inverse design, which tries to extract the ideal structure according to specified requirements, remains a complex process. The current study looks at the forward prediction of mechanical response and the inverse design of 2D random network metamaterial unit cells for use in aperiodic combinatorial designs. The associated mechanical properties of stiffness and Poisson's ratio in two directions were numerically simulated for the unit cells. The configuration of random network designs that provide the least complicated unit cells with the greatest range of mechanical properties were selected. A dataset of such unit cells and their mechanical responses was then created for training machine learning models. A forward predictor of their mechanical response through a regression model was trained, to accelerate the production of the datasets and assist in the inverse design modelling. It was suggested to model the inverse design issue in a probabilistically generative manner, allowing for an interpretable investigation of the complicated structure–performance connection. A variational autoencoder (VAE) was trained to map complex microstructures into a low-dimensional, continuous, and organized latent space based on their mechanical response. A systematic approach to train machine learning models was investigated based on hyperparameter optimization methods, with cross-validation and a search space that expands over the model hyperparameters and data analysis alternatives. The trained forward predictor and generative model were evaluated, and the reported R^2 values were 0.99 and 0.96, respectively. Compared to numerical simulations, the estimation of mechanical properties through the regressor resulted in a hundredfold acceleration of the process. The trained deep generative model can be an effective tool for expediting the creation of RN unit cells. On-demand inverse creation of metamaterials for both observable and intentionally designed mechanical responses is achievable. To illustrate a practical use of such unit cells, a technique for aperiodic combinatorial design of unit cells for 2D form matching on two directions and all four sides was also provided. The mechanical properties of the unit cells, as well as the combinatorial design capabilities, were evaluated using experimental tests of 3D printed specimens. The experimental results of the unit cells were in good agreement with the numerical simulations. Similar forms to those programmed were achieved by tensioning the combinatorial patterns, albeit with a less degree of distortion. Overall, the findings show the RN unit cells' potential for combinatorial design.

Nature has always had randomness in many ways. Natural cellular structures, specifically, have used randomized microarchitectures to form mechanically efficient materials, such as wood, sponge, and trabecular bone. Mechanical metamaterials are a class of artificial microarchitected materials designed to carry tensions and compressions with lightweight. However, there had been some limitations regarding the range of mechanical properties and geometrical parameters of regular honeycomb mechanical metamaterials. Also, it is not possible for extreme auxetic honeycomb mechanical metamaterials, which are with extreme negative Poisson’s ratio, to have a high level of stiffness. The addition of randomness had been proven to extend the properties of mechanical metamaterials.

In this project, through the approach of the combination of bending- and stretching-dominated structures, based on the level of nodal connectivity of the structures, we aimed to explore the range of properties in both the in-plane and the out-of-plane contexts. In terms of in-plane properties, we proposed a simple design that can achieve extreme auxetic or double-side auxetic lattices, the corner-plus design. The design successfully created lattices with an over -0.8 negative Poisson’s ratio value and a 6 to 10 times level of stiffness as regular honeycomb metamaterials. Through recognition and rearrangement of the openings, the peninsula-like region that expands under tension, over one million different lattices are generated with extreme negative Poisson’s ratio values and a high level of stiffness. The design had expanded the stiffness versus Poisson’s ratio region compared to the ordinary materials and regular honeycomb mechanical metamaterials.

Furthermore, in total six meta-plates were designed from the selected metamaterials and additively manufactured for the investigation of out-of-plane behavior. Experimentally and computationally, their deformation and curvatures presented that the induced curvature along the transverse direction was adjustable, which can be manipulated with the level of connectivity and the value of Poisson’s ratio. Openings in these meta-plates also played a big role in the formulation of surface contours and induced curvatures both locally and globally.

In general, openings had evident contributions to the auxeticity and the unique surface contours, which can be a key factor in the future design of the random mechanical metamaterials. The extreme auxeticity and the modular surface topology can be further implemented into the design of wearable prostheses or medical implants for future applications.

Integration of the sensory systems inside patient-specific medical devices (e.g., implants or prosthesis) can provide additional measurement data at different time points. This can result in improving the traditional designs of those medical devices, and consequently leading to increasing the patient’s comfort and quality of life. By doing measurements directly on or inside the patient, information can be gathered more specifically, which is important for improving detection and diagnosis. Next to that, it enables better decision-making for therapies, treatments and the allocation of prosthesis, causing improved rehabilitation. Problems with the comfort and fitting of the prosthesis can have a big impact on the life of the patient wearing the prosthetic. If there is lack of comfort, the patient is less likely to wear the device and that could limit the rehabilitation and satisfaction about their life in multiple areas. To get a better insight in the transtibial prosthesis in particular, and possibly solve the problems that occur, measurements can be done at the interface of the patient and the socket of this prosthesis. The data can be used to visualize the problem more accurately and, as a result, the response and treatment process can be chosen in a more precise manner. First, a case study about the lower limb prosthesis was conducted. Several questions will be reviewed, including defining elements of the satisfaction of patients about their prosthesis, what current existing problems with prosthesis are and the reasons of patients why they are not satisfied with their prosthesis. Then, two types of sensors will be used to visualise the problems occurring at the interface of the socket and the residual limb. One type of sensor is the piezoelectric pressure sensor. It is used to map the pressure on the limb inside the socket. Several experiments have been done with an existing 3D printed socket design and the data is compared to a finite element model that predicts stress distributions to validate this model. The model itself was changed on multiple areas to get closer to test conditions and improve model outputs. To be able to improve such models, the test-procedure should be further revised. After that, thermistors were used to measure the temperature within a 3D printed socket. A design for a new socket has been made to actively use the temperature data and cool the residual limb inside the socket when needed. This is done by integrating air-channels within the structure of the 3D printed socket and air is forced through those channels with a power-fan. The design has been validated with several tests and it is improved based on that. Those tests, calculations and estimations have shown that the current design may be able to cool the residual limb when further improved and optimized. In the end, several suggestions and recommendations for improvements were provided. Some of those improvements could be made in the size of the air-channels and the interface between socket and residual limb. Finally, present and potential (ongoing) developments on how to change the patient's condition, as well as how to apply promising developments in innovative ways, will be addressed.

The conventional fabrication process of prosthetic sockets is known for being labor-intensive and time-consuming. It takes the user to wait 2-5 weeks to receive the prosthetic socket. In addition, most of the conventional sockets have fixed volumes that do not consider the volume fluctuations that the user experience daily because of muscle activities and comorbid medical conditions. 3D printing has shown promising results in producing lower limb sockets; however, upper limb sockets are overlooked. The goal of this master thesis is to design and fabricate a volume-adjustable, affordable, 3D printable transradial, prosthetic socket. In this research, the V3D (Volume-adjustable, 3D printable) socket has been developed with a material cost of only €30. The socket provides a volume-adjustable closure system around the residual limb, easiness of donning and doffing without skin shearing, full elbow extension, high range of flexion, low weight, and breathability. The socket was designed to be able to withstand a load of 50N that can be applied axially or transversely at the tip of the socket without breakage. The designed socket was 3D printed using the Fused Deposition Modeling (FDM) printing technique, from tough Polylactic Acid (tough PLA). Mechanical and human assessments have been conducted to evaluate the strength, function, and comfort of the developed socket. Results have shown that the socket managed to withstand a load up to 100N that was applied axially and transversely, respectively, without showing any signs of damage. During testing the socket with 5 participants for evaluating its comfort and function, the socket has succeeded to achieve full contact with the residual limbs, while offering volume-adjustability that accommodated the differences in size and properties of the residual limbs among participants. Not only that, but also, the socket succeeded to allow full elbow extension, a range of flexion up to 95°, and donning and doffing in less than 10 seconds without applying any shear forces on the skin. The developed V3D socket has proven the possibility to 3D print reliable sockets using the FDM printing technique with a total labor time of 4 hours per socket, and a total fabrication time of 5 days with a material cost of only €30. In addition, the socket has proved that it can be fitted using a caliper in case 3D scanners are not available. That advantage makes producing the V3D socket feasible in communities that do not have access to 3D scanners. Furthermore, the socket can be parameterized such that users with similar geometry and comparable sizes of residual limbs can fit the same socket, which would make the fabrication process less labor-intensive and less time-consuming as it was aimed for.