Snake Robot with Motion Based on Shape Memory Alloy Spring-Shaped Actuators

Cortez, Ricardo; Sandoval-Chileño, Marco Antonio; Lozada-Castillo, Norma; Luviano-Juárez, Alberto

doi:10.3390/biomimetics9030180

Cited by 3 publications

(2 citation statements)

References 63 publications

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“…Once being heated beyond its austenite finish temperature, proper annealed SMA would regain its memorized shape at a much higher annealing temperature, eliminating its macroscopic deformation occurring in the martensitic phase. Shape memory alloys with large strains up to 8% have been commonly used in actuation elements, providing a significant driving force during their stroke course [22][23][24]. As such, SMA wires with submillimeter diameters and appropriate annealing for SME actuation greatly appeal to researchers working on developing actively controllable microcatheters for blood vessels with inner diameters of several millimeters [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Design, Modeling, and Experimental Validation of an Active Microcatheter Driven by Shape Memory Effects

Li,

Zhang,

Ren

et al. 2024

Micromachines

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Microcatheters capable of active guidance have been proven to be effective and efficient solutions to interventional surgeries for cardiovascular and cerebrovascular diseases. Herein, a novel microcatheter made of two biocompatible materials, shape memory alloy (SMA) and polyethylene (PE), is proposed. It consists of a reconfigurable distal actuator and a separate polyethylene catheter. The distal actuator is created via embedding U-shape SMA wires into the PE base, and its reconfigurability is mainly dominated by the shape memory effect (SME) of SMA wires, as well as the effect of thermal mismatch between the SMA and PE base. A mathematical model was established to predict the distal actuator’s deformation, and the analytical solutions show great agreement with the finite element results. Structural optimization of such microcatheters was carried out using the verified analytical model, followed by fabrication of some typical prototypes. Experimental testing of their mechanical behaviors demonstrates the feasibility of the structural designs, and the reliability and accuracy of the mathematical model. The active microcatheter, together with the prediction model, will lay a solid foundation for rapid development and optimization of active navigation strategies for vascular interventions.

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Section: Introductionmentioning

confidence: 99%

Design, Modeling, and Experimental Validation of an Active Microcatheter Driven by Shape Memory Effects

Li,

Zhang,

Ren

et al. 2024

Micromachines

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“…Flexible and lightweight soft actuators are suitable for muscle mimicry and soft wearable robots, making them for mimicking knee movement. A notable example of a soft actuator is the shape memory alloy (SMA) [24,25]. The SMA deforms above its transformation temperature when heated.…”

Section: Introductionmentioning

confidence: 99%

Shape Memory Alloys Patches to Mimic Rolling, Sliding, and Spinning Movements of the Knee

Seo,

Kang,

Han

2024

Biomimetics

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Every year, almost 4 million patients received medical care for knee osteoarthritis. Osteoarthritis involves progressive deterioration or degenerative changes in the cartilage, leading to inflammation and pain as the bones and ligaments are affected. To enhance treatment and surgical outcomes, various studies analyzing the biomechanics of the human skeletal system by fabricating simulated bones, particularly those reflecting the characteristics of patients with knee osteoarthritis, are underway. In this study, we fabricated replicated bones that mirror the bone characteristics of patients with knee osteoarthritis and developed a skeletal model that mimics the actual movement of the knee. To create patient-specific replicated bones, models were extracted from computerized tomography (CT) scans of knee osteoarthritis patients. Utilizing 3D printing technology, we replicated the femur and tibia, which bear the weight of the body and support movement, and manufactured cartilage capable of absorbing and dispersing the impact of knee joint loads using flexible polymers. Furthermore, to implement knee movement in the skeletal model, we developed artificial muscles based on shape memory alloys (SMAs) and used them to mimic the rolling, sliding, and spinning motions of knee flexion. The knee movement was investigated by changing the SMA spring’s position, the number of coils, and the applied voltage. Additionally, we developed a knee-joint-mimicking system to analyze the movement of the femur. The proposed artificial-skeletal-model-based knee-joint-mimicking system appears to be applicable for analyzing skeletal models of knee patients and developing surgical simulation equipment for artificial joint replacement surgery.

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Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach

Tymoshchuk,

Yasniy,

Maruschak

et al. 2024

Computers

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This paper investigates the use of machine learning methods to predict the loading frequency of shape memory alloys (SMAs) based on experimental data. SMAs, in particular nickel-titanium (NiTi) alloys, have unique properties that restore the original shape after significant deformation. The frequency of loading significantly affects the functional characteristics of SMAs. Experimental data were obtained from cyclic tensile tests of a 1.5 mm diameter Ni55.8Ti44.2 wire at different loading frequencies (0.1, 0.5, 1.0, and 5.0 Hz). Various machine learning methods were used to predict the loading frequency f (Hz) based on input parameters such as stress σ (MPa), number of cycles N, strain ε (%), and loading–unloading stage: boosted trees, random forest, support vector machines, k-nearest neighbors, and artificial neural networks of the MLP type. Experimental data of 100–140 load–unload cycles for four load frequencies were used for training. The dataset contained 13,365 elements. The results showed that the MLP neural network model demonstrated the highest accuracy in load frequency classification. The boosted trees and random forest models also performed well, although slightly below MLP. The SVM method also performed quite well. The KNN method showed the worst results among all models. Additional testing of the MLP model on cycles that were not included in the training data (200th, 300th, and 1035th cycles) showed that the model retains high efficiency in predicting load frequency, although the accuracy gradually decreases on later cycles due to the accumulation of structural changes in the material.

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Snake Robot with Motion Based on Shape Memory Alloy Spring-Shaped Actuators

Cited by 3 publications

References 63 publications

Design, Modeling, and Experimental Validation of an Active Microcatheter Driven by Shape Memory Effects

Design, Modeling, and Experimental Validation of an Active Microcatheter Driven by Shape Memory Effects

Shape Memory Alloys Patches to Mimic Rolling, Sliding, and Spinning Movements of the Knee

Loading Frequency Classification in Shape Memory Alloys: A Machine Learning Approach

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