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.

Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks

Human skin is characterized by rough, elastic, and uneven features that are difficult to recreate using conventional manufacturing technologies and rigid materials. The use of soft materials is a promising alternative to produce devices that mimic the tactile capabilities of biological tissues. Although previous studies have revealed the potential of fillers to modify the properties of composite materials, there is still a gap in modeling the conductivity and mechanical properties of these types of materials. While traditional Finite Element approximations can be used, these methodologies tend to be highly demanding of time and processing power. Instead of this approach, a data-driven learning-based approximation strategy can be used to generate prediction models via neural networks. This paper explores the fabrication of flexible nanocomposites using polydimethylsiloxane (PDMS) with different single-walled carbon nanotubes (SWCNTs) loadings (0.5, 1, and 1.5 wt.%). Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models were formulated, trained, and tested to obtain the predictive sequence data of out-of-plane quasistatic mechanical tests. Finally, the model learned is applied to a dynamic system using the Kelvin-Voight model and the phenomenon known as the bouncing ball. The best predictive results were achieved using a nonlinear activation function in the SRNN model implementing two units and 4000 epochs. These results suggest the feasibility of a hybrid approach of analogy-based learning and data-driven learning for the design and computational analysis of soft and stretchable nanocomposite materials.