MEMS Applications of NiTi Based Shape Memory Alloys: A Review
About: This article is published in Micro and Nanosystems.The article was published on 2017-06-07. It has received 55 citations till now.
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TL;DR: In this paper, the effects of laser welding process on the functional properties of NiTi and related alloys are investigated, and the impact of the post-weld heat treatment (PWHT) is studied as an effective solution to improve the downsides of the Laser welding process.
93 citations
01 Jan 2020
TL;DR: In this review, the implantable flexible nerve electrodes (IFNEs) based on functionalized substrates, intelligent electrodes, and innovative structures are elaborated and discussed and summarized their various applications in neural prosthesis and neural signal recording.
Abstract: Implantable electrical devices offer a variety of potential diagnostic options and treatments in different medical fields. Especially in the absence of specific drugs, the implantable nerve electrodes (INEs) are one of the main treatments for neurological diseases such as epilepsy, Parkinson’s disease, Alzheimer’s disease, etc. INEs are helpful in studying and regulating nervous system via recording nerve electrical signals or stimulating nerve tissue, but the mechanical mismatch between rigid electrode and soft biological tissue is a critical challenge for long-term implantation. The advances in micromachining technologies and materials have greatly promoted the development of INEs, such as enhanced biocompatibility, reduced foreign body reactions, and structural innovation. In particular, the mechanical performances of flexible implantable electrode and soft biological tissue matched better with less tissue damage. In this review, the implantable flexible nerve electrodes (IFNEs) based on functionalized substrates, intelligent electrodes, and innovative structures are elaborated and discussed. We summarized their various applications in neural prosthesis and neural signal recording. We also discussed the questions and possible methods for developing future IFNEs in the perspective.
48 citations
Cites background from "MEMS Applications of NiTi Based Sha..."
...SMAs have the advantages of high mechanical perform ance, high power-to-weight ratio, resistance of large deformation, and biocompatibili ty, providing highly promising solutions for microdevices [76]....
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TL;DR: A prediction model based on artificial neural network (ANN) is developed to generate a nonlinear map between inputs and outputs of the AM process, also useful for dealing with the settings of the optimal operational parameters.
Abstract: Shape memory alloys (SMAs) have been applied for various applications in the fields of aerospace, automotive, and medical. Nickel-titanium (NiTi) is the most well-known alloy among the others due to its outstanding functional characteristics including superelasticity (SE) and shape memory effect (SME). These particular properties are the result of the reversible martensite-to-austenite and austenite-to-martensite transformations. In recent years, additive manufacturing (AM) has provided a great opportunity for fabricating NiTi products with complex shapes. Many researchers have been investigating the AM process to set the optimal operational parameters, which can significantly affect the properties of the end-products. Indeed, the functional and mechanical behavior of printed NiTi parts can be tailored by controlling laser power, laser scan speed, and hatch spacing having them a crucial role in properties of 3D-printed parts. In particular, the effect of the input parameters can significantly alter the mechanical properties such as strain recovery rates and the transformation temperatures; therefore, using suitable parameter combination is of paramount importance. In this framework, the present study develops a prediction model based on artificial neural network (ANN) to generate a nonlinear map between inputs and outputs of the AM process. Accordingly, a prototyping tool for the AM process, also useful for dealing with the settings of the optimal operational parameters, will be built, tested, and validated.
47 citations
TL;DR: In this paper, the authors investigated the laser welding of NiTi thin sheets with a high-power diode laser (HPDL) and reported microstructural, functionality, and mechanical effects of the process in following
Abstract: NiTi shape memory alloy (SMA) are widely applied in many industrial domains, such as biomedical, aerospace, automotive and power plants, due to its outstanding functionality including superelasticity (SE) and shape memory effect (SME) The machining process of this material is challenging with a lot of barriers Accordingly, joining techniques can be an alternative approach to design the shape memory components with more flexibility Among all methods, laser welding process is a reliable and economical technique for joining of NiTi alloys However, thermal process influences strongly on the strength and functionality of the NiTi welded joints in the Heat Affected Zone (HAZ) and the Fusion Zone (FZ) Indeed, the transformation temperature of NiTi alloy can be altered due to varying in the material composition Therefore, controlling of the operational parameters, including laser power, scan speed or focal distance lead to an effective improvement in the mechanical and the functional behavior of NiTi joints It consequently enhances the weldability of this material This current study investigates the laser welding of NiTi thin sheets with a High-Power Diode Laser (HPDL) and reports microstructural, functionality, and mechanical effects of the process in following
40 citations
TL;DR: In this article, a finite element model was used to predict the thermal behavior induced by the laser welding process, which can reduce the heat affected and the fusion regions and thus result in a better weld.
Abstract: Laser welding of NiTi alloy is a challenging process since it strongly affects the functionality of the material in the heat affected and fusion zones. In fact, the inherent thermal process can remarkably change the transformation temperature of NiTi alloy in the welding zone because of variation in the material composition. Accordingly, the laser parameters such as laser power and velocity effectively determine the quality of the welded component. The functional and mechanical behavior of the resulting welded NiTi parts can also be effectively improved by controlling laser parameters, and consequently, improve the weldability quality. The purpose of the present study was to establish a reliable finite element model to predict the thermal behavior induced by the laser welding process. To this end, a numerical model was employed to estimate the optimum laser parameters, which can reduce the heat affected and the fusion regions and thus result in a better weld. The results of the finite element model show good accuracy compared to the experimental results including the transient temperature and the dimension of the heat affected and fusion zones. In addition, an Artificial Neural Network (ANN) approach was applied, as a predictable tool, to perform a nonlinear mapping between inputs and outputs of the welding process in order to find the optimum laser parameters.
30 citations