Shape memory NiTi powders produced by electrode induction melting gas atomization (EIGA) method for additive manufacturing

2021 ◽  
Author(s):  
Junwei Wang ◽  
Dingyong He ◽  
Xu Wu ◽  
Xingye Guo ◽  
Zhen Tan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Induction Melting ◽  
Gas Atomization

Compositional Optimization of In718 Superalloy Powder for Additive Manufacturing

2018 ◽  
Vol 941 ◽  
pp. 2167-2172
Author(s):  
Che Yi Lin ◽  
Hui Yum Bor ◽  
Chao Nan Wei ◽  
Chien Hung Liao
Keyword(s):  
Particle Size ◽  
High Temperature ◽  
Additive Manufacturing ◽  
Vacuum Induction Melting ◽  
Induction Melting ◽  
Gas Atomization ◽  
In718 Superalloy ◽  
Sieving Process ◽  
Superalloy Powder ◽  
Metal Droplets

In this research, a composition optimized In718 superalloy powder suitable for additive manufacturing has been developed by using the vacuum induction melting gas atomization (VIGA) and the powder sieving process. VIGA which combines the vacuum induction melting (VIM) and gas atomization (GA) processes uses high pressure inert gas to atomize the metal melt formed by VIM to form metal droplets. These metal droplets are solidified to form metal powders during the falling process in the atomized chamber. After the sieving process, the mean particle size D50 of the powder is less than 35 μm and the particle size distribution (PSD) ranges from 10 to 55 μm (D10~D90). Besides, the produced powder has high flowability (ICarr ≦15), which is suitable for selective laser melting (SLM) additive manufacturing (AM). After the SLM process, the tensile tests are conducted at room temperature and high temperature of 650°C. The results show that the high temperature properties of the optimized In718 superalloy are superior to the commercial In718 superalloy.

scholarly journals Design of Shape Memory Thermoplastic Material Systems for FDM-Type Additive Manufacturing

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4254
Author(s):  
Paulina A. Quiñonez ◽  
Leticia Ugarte-Sanchez ◽  
Diego Bermudez ◽  
Paulina Chinolla ◽  
Rhyan Dueck ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Thermomechanical Processing ◽  
Material Selection ◽  
Shape Memory Polymer ◽  
Fused Filament Fabrication ◽  
Materials Development ◽  
Thermoplastic Material ◽  
Polymer Systems ◽  
Injection Molded

The work presented here describes a paradigm for the design of materials for additive manufacturing platforms based on taking advantage of unique physical properties imparted upon the material by the fabrication process. We sought to further investigate past work with binary shape memory polymer blends, which indicated that phase texturization caused by the fused filament fabrication (FFF) process enhanced shape memory properties. In this work, two multi-constituent shape memory polymer systems were developed where the miscibility parameter was the guide in material selection. A comparison with injection molded specimens was also carried out to further investigate the ability of the FFF process to enable enhanced shape memory characteristics as compared to other manufacturing methods. It was found that blend combinations with more closely matching miscibility parameters were more apt at yielding reliable shape memory polymer systems. However, when miscibility parameters differed, a pathway towards the creation of shape memory polymer systems capable of maintaining more than one temporary shape at a time was potentially realized. Additional aspects related to impact modifying of rigid thermoplastics as well as thermomechanical processing on induced crystallinity are also explored. Overall, this work serves as another example in the advancement of additive manufacturing via materials development.

Vacuum induction melting of NiTi shape memory alloys in graphite crucible

2007 ◽  
Vol 465 (1-2) ◽  
pp. 44-48 ◽  
Author(s):  
Niraj Nayan ◽  
Govind ◽  
C.N. Saikrishna ◽  
K. Venkata Ramaiah ◽  
S.K. Bhaumik ◽  
...  
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Graphite Crucible ◽  
Vacuum Induction Melting ◽  
Induction Melting ◽  
Niti Shape Memory Alloys

The electron beam freeform fabrication of NiTi shape memory alloys. Part I: Microstructure and physical–chemical behavior

2020 ◽  
pp. 146442072097505
Author(s):  
RPM Guimarães ◽  
F Pixner ◽  
G Trimmel ◽  
J Hobisch ◽  
T Rath ◽  
...  
Keyword(s):  
Electron Beam ◽  
Additive Manufacturing ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Nickel Titanium ◽  
Freeform Fabrication ◽  
Austenitic Transformation ◽  
Columnar Grains ◽  
One Step ◽  
Niti Shape Memory Alloys

Nickel–titanium alloys are the most widely used shape memory alloys due to their outstanding shape memory effect and superelasticity. Additive manufacturing has recently emerged in the fabrication of shape memory alloy but despite substantial advances in powder-based techniques, less attention has been focused on wire-based additive manufacturing. This work reports on the preliminary results for the process-related microstructural and phase transformation changes of Ni-rich nickel–titanium alloy additively manufactured by wire-based electron beam freeform fabrication. To study the feasibility of the process, a simple 10-layer stack structure was successfully built and characterized, exhibiting columnar grains and achieving one-step reversible martensitic–austenitic transformation, thus showing the potential of this additive manufacturing technique for processing shape memory alloys.

Additive Manufacturing of Shape Memory Polymer Composites for Futuristic Technology

2021 ◽  
Author(s):  
Jigar Patadiya ◽  
Adwait Gawande ◽  
Ganapati Joshi ◽  
Balasubramanian Kandasubramanian
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Polymer Composites ◽  
Shape Memory Polymer

scholarly journals In Search of the Optimal Conditions to Process Shape Memory Alloys (NiTi) Using Fused Filament Fabrication (FFF)

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4718
Author(s):  
Pedro Carreira ◽  
Fábio Cerejo ◽  
Nuno Alves ◽  
Maria Teresa Vieira
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Sintering Temperature ◽  
Production Costs ◽  
Layer By Layer ◽  
Secondary Phases ◽  
Fused Filament Fabrication ◽  
Near Net Shape ◽  
Niti Shape Memory Alloys

This research was performed so as to investigate the additive manufacturing of NiTi shape memory alloys, which is associated with direct processes, such as selective laser melting. In addition to its expensive production costs, NiTi readily undergoes chemical and phase modifications, mainly as a result of Ni loss during processing as a result of high temperatures. This research explores the potential usefulness of NiTi as well as its limitations using indirect additive processes, such as fused filament fabrication (FFF). The first step was to evaluate the NiTi critical powder volume content (CPVC) needed to process high-quality filaments (via extrusion). A typical 3D printer can build a selected part/system/device layer-by-layer from the filaments, followed by debinding and sintering (SDS), in order to generate a near-net-shape object. The mixing, extruding (filament), printing (shaping), debinding, and sintering steps were extensively studied in order to optimize their parameters. Moreover, for the sintering step, two main targets should be met, namely: the reduction of contamination during the process in order to avoid the formation of secondary phases, and the decrease in sintering temperature, which also contributes to reducing the production costs. This study aims to demonstrate the possibility of using FFF as an additive manufacturing technology for processing NiTi.

Shape memory TiNi powders produced by plasma rotating electrode process for additive manufacturing

2017 ◽  
Vol 27 (12) ◽  
pp. 2647-2655 ◽  
Author(s):  
Gang CHEN ◽  
Shao-yang ZHAO ◽  
Ping TAN ◽  
Jing-ou YIN ◽  
Quan ZHOU ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Electrode Process ◽  
Rotating Electrode

Additive manufacturing of shape memory polymers: effects of print orientation and infill percentage on shape memory recovery properties

2020 ◽  
Vol 26 (9) ◽  
pp. 1593-1602
Author(s):  
Jorge Villacres ◽  
David Nobes ◽  
Cagri Ayranci
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Shape Recovery ◽  
Fused Deposition Modeling ◽  
Large Strain ◽  
Shape Memory Polymers ◽  
Polymeric Materials ◽  
Content Type ◽  
Fused Deposition ◽  
Memory Recovery

Purpose The purpose of this paper is to study the shape memory properties of SMP samples produced through a MEAM process. Fused deposition modeling or, as it will be referred to in this paper, material extrusion additive manufacturing (MEAM) is a technique in which polymeric materials are extruded though a nozzle creating parts via accumulation and joining of different layers. These layers are fused together to build three-dimensional objects. Shape memory polymers (SMP) are stimulus responsive materials, which have the ability to recover their pre-programmed form after being exposed to a large strain. To induce its shape memory recovery movement, an external stimulus such as heat needs to be applied. Design/methodology/approach This project investigates and characterizes the influence of print orientation and infill percentage on shape recovery properties. The analyzed shape recovery properties are shape recovery force, shape recovery speed and time elapsed before activation. To determine whether the analyzed factors produce a significant variation on shape recovery properties, t-tests were performed with a 95% confidence factor between each analyzed level. Findings Results proved that print angle and infill percentage do have a significant impact on recovery properties of the manufactured specimens. Originality/value The manufacturing of SMP objects through a MEAM process has a vast potential for different applications; however, the shape recovery properties of these objects need to be analyzed before any practical use can be developed. These have not been studied as a function of print parameters, which is the focus of this study.

scholarly journals Harnessing irreversible thermal strain for shape memory in polymer additive manufacturing

2019 ◽  
Vol 136 (47) ◽  
pp. 48239 ◽  
Author(s):  
Tone P. D'Amico ◽  
Connor Barrett ◽  
Joseph Presing ◽  
Roshni Patnayakuni ◽  
Masoumeh Pourali ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Shape Memory ◽  
Thermal Strain ◽  
Polymer Additive
Sign in / Sign up

Export Citation Format

Share Document