Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning.

Shape memory alloy (SMA) actuators are an important application of smart materials for robotics. However, the nonlinear behavior of SMA leads to difficulties in real-time simulations using numerical methods. Artificial Intelligence can be used to bypass this problem. In this paper, we study several neural networks (NNs) to model the superelastic or pseudo-elasticity effect (SEE) as well as the shape memory effect (SME) used in SMA. Focusing on antagonistic actuating, we first model a single wire to train the best NN with the proper characteristics that fit the behavior of SEE. Then, we model the SME of two linear antagonistic SMA wires used as an actuator. In both systems, single and antagonistic wires, we train the networks to obtain the stress-strain diagrams representing the behavior. The network type and training algorithm are key factors and are evaluated depending on the RMSE values. As a result, we find that the long short-term memory NN, used with a regression layer on standardized data sets, models the butterfly-shaped behavior of the actuator system with less RMSE value.

There is a growing body of evidences that brain surrogates will be of great interest for researchers and physicians in the medical field. They are currently mainly used for education and training purposes or to verify the appropriate functionality of medical devices. Depending on the purpose, a variety of materials have been used with specific and accurate mechanical and biophysical properties, More recently they have been used to assess the biocompatibility of implantable devices, but they are still not validated to study the migration of leaching components from devices. This minireview shows the large diversity of approaches and uses of brain phantoms, which converge punctually. All these phantoms are complementary to numeric models, which benefit, reciprocally, of their respective advances. It also suggests avenues of research for the analysis of leaching components from implantable devices.