Target shape optimization of functionally graded shape memory alloy compliant mechanisms

Jovanova, Jovana; Frecker, Mary; Hamilton, Reginald F.; Palmer, Todd

doi:10.1177/1045389x17733057

Cited by 10 publications

(8 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[23] To date, creating microstructural gradient Ni-Ti components can be provided by gradient annealing, additive manufacturing, powder metallurgy, surface treatment such as ultrasonic peening and laser surface heating, as well as particle irradiation, etc. [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53]…”

Section: Compositional Gradient Ni-ti Shape Memory Alloysmentioning

confidence: 99%

Brief Overview of Functionally Graded NiTi‐Based Shape Memory Alloys

et al. 2023

View full text Add to dashboard Cite

Shape memory alloys (SMAs) are kinds of smart materials that have superior properties such as shape memory effect and superelasticity, which have the most potential applications in various fields, especially in aerospace, naval, automobile and biomedicine industries, etc. Nevertheless, the inherent natures of shape memory alloys are characterized by the smaller transformation temperature intervals and transformation stress intervals, which make the devices have poor control ability. To achieve the accurate controllability for progressive movement, creating functionally graded shape memory alloys has been adopted. Herein, the classification, fabrications, microstructural features, and performances of functionally graded shape memory alloys are reviewed. For comparison, the creation of various gradients in shape memory alloys can widen the transformation temperature intervals and transformation stress intervals to some extent. In addition, the formation of the compositional, microstructural, or geometrical gradient also contributes to the generation of excellent performances such as the four‐way shape memory effect, higher strength, and larger temperature window for the stress‐assisted two‐way shape memory effect, etc. Such superior functions promote a wider application range of shape memory alloys.

show abstract

Section: Compositional Gradient Ni-ti Shape Memory Alloysmentioning

confidence: 99%

Brief Overview of Functionally Graded NiTi‐Based Shape Memory Alloys

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Contributions have been paid to L-DEDed NiTi by researchers worldwide, mainly could include two aspects over the last decade [3,15]. Synthesizing NiTi alloys with different alloying element concentrations in situ by using a flexible and controllable powder feeding method in the DED process [3,8,12,[15][16][17][18][19][20][21]. It is a great innovation in NiTi alloys and component fabrication, resulting in a series of new theories, new methods, and unprecedented properties [17].…”

Section: Introductionmentioning

confidence: 99%

Formation mechanism of inherent spatial heterogeneity of microstructure and mechanical properties of NiTi SMA prepared by laser directed energy deposition

Luo

Zheng

et al. 2023

Int. J. Extrem. Manuf.

View full text Add to dashboard Cite

Ni51Ti49 at. % bulk with a large volume was prepared by laser-directed energy deposition to reveal the microstructure evolution, phase distribution, and mechanical properties. It is found that the localized remelting, reheating and heat accumulation during DED leads to the spatial heterogeneous distribution of columnar crystal and equiaxed crystal, a gradient distribution of Ni4Ti3 precipitates along the building direction, and preferential formation of Ni4Ti3 precipitates in the columnar zone. The Austenite transformation finish temperature (Af) varies from -12.65°C (Z=33mm) to 60.35°C (Z=10mm), corresponding to tensile yield strength (σ0.2) changed from 120 ± 30 MPa to 570 ± 20 MPa, and functional properties changed from shape memory effect to superelasticity at room temperature. The sample in the Z = 20.4 mm height has the best plasticity of 9.6% and the best recoverable strain of 4.2 %. This work provided insights and guidelines for the spatial characterization of DED NiTi. 

show abstract

“…According to Liu et al (2012), self-folding is defined as a process of assembly that exclusively determines the path to fold the 2D pattern into a target 3D geometry. Many types of active materials have been investigated and applied to realize origami-inspired selffolding structures, including dielectric elastomers (DE) (Ahmed et al, 2014(Ahmed et al, , 2017aCarpi et al, 2011), electroactive polymers (EAPs), magnetoactive elastomers (MAEs), shape memory alloys (SMAs) (Hernandez et al, 2017;Jovanova et al, 2019;Katsumata et al, 2017;Zhang and Ren, 2019), shape memory polymers (SMPs) responsive to thermal field (Chen et al, 2019;Li et al, 2014;Yang et al, 2016), and SMPs triggered by light (Cehula and Pru˚sˇa, 2020;Laflin et al, 2012;Liu et al, 2014). Given that each active material exhibits its own advantages and disadvantages, the most appropriate active material for a particular application depends on the specific needs of the application.…”

Section: Introductionmentioning

confidence: 99%

A two-stage design optimization framework for multifield origami-inspired structures

Wei

Ahmed

Hong

et al. 2021

Journal of Intelligent Material Systems and Structures

Self Cite

View full text Add to dashboard Cite

Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

show abstract

Target shape optimization of functionally graded shape memory alloy compliant mechanisms

Cited by 10 publications

References 41 publications

Brief Overview of Functionally Graded NiTi‐Based Shape Memory Alloys

Brief Overview of Functionally Graded NiTi‐Based Shape Memory Alloys

Formation mechanism of inherent spatial heterogeneity of microstructure and mechanical properties of NiTi SMA prepared by laser directed energy deposition

A two-stage design optimization framework for multifield origami-inspired structures

Contact Info

Product

Resources

About