Advances in Selective Laser Melting of Nitinol Shape Memory Alloy Part Production

形状记忆合金 选择性激光熔化 材料科学 假弹性 微观结构 惰性 近净形状 制作 智能材料 钛镍合金 合金 复合材料 马氏体 病理 物理 医学 替代医学 量子力学
作者
Josiah Cherian Chekotu,Robert Groarke,Kevin O’Toole,Dermot Brabazon
出处
期刊:Materials [Multidisciplinary Digital Publishing Institute]
卷期号:12 (5): 809-809 被引量:59
标识
DOI:10.3390/ma12050809
摘要

Nitinol (nickel-titanium or Ni-Ti) is the most utilized shape memory alloy due to its good superelasticity, shape memory effect, low stiffness, damping, biocompatibility, and corrosion resistance. Various material characteristics, such as sensitivity to composition and production thermal gradients, make conventional methods ineffective for the manufacture of high quality complex Nitinol components. These issues can be resolved by modern additive manufacturing (AM) methods which can produce net or near-net shape parts with highly precise and complex Nitinol structures. Compared to Laser Engineered Net Shape (LENS), Selective Laser Melting (SLM) has the benefit of more easily creating a high quality local inert atmosphere which protects chemically-reactive Nitinol powders to a higher degree. In this paper, the most recent publications related to the SLM processing of Nitinol are reviewed to identify the various influential factors involved and process-related issues. It is reported how powder quality and material composition have a significant effect on the produced microstructures and phase transformations. The effect of heat treatments after SLM fabrication on the functional and mechanical properties are noted. Optimization of several operating parameters were found to be critical in fabricating Nitinol parts of high density. The importance of processing parameters and related thermal cooling gradient which are crucial for obtaining the correct phase structure for shape memory capabilities are also presented. The paper concludes by presenting the significant findings and areas of prospective future research in relation to the SLM processing of Nitinol.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
繁星发布了新的文献求助10
刚刚
1秒前
华仔应助鹤轩采纳,获得10
1秒前
Eddy发布了新的文献求助10
2秒前
2秒前
2秒前
2秒前
BettyNie完成签到 ,获得积分10
4秒前
myf完成签到 ,获得积分10
4秒前
5秒前
李爱国应助小乔采纳,获得10
6秒前
Leo发布了新的文献求助10
7秒前
量子星尘发布了新的文献求助10
8秒前
8秒前
积极的邴发布了新的文献求助10
9秒前
zzz发布了新的文献求助10
12秒前
13秒前
香蕉觅云应助liu采纳,获得10
14秒前
15秒前
15秒前
弓長玉王令完成签到,获得积分10
17秒前
17秒前
漂亮绮彤发布了新的文献求助10
18秒前
量子星尘发布了新的文献求助150
18秒前
18秒前
coco发布了新的文献求助10
20秒前
21秒前
慕青应助ee采纳,获得10
24秒前
25秒前
浮游应助2222233采纳,获得10
25秒前
maomao完成签到,获得积分10
25秒前
26秒前
浮游应助跳跃幻枫采纳,获得10
26秒前
浮游应助跳跃幻枫采纳,获得10
26秒前
浮游应助跳跃幻枫采纳,获得10
26秒前
Owen应助跳跃幻枫采纳,获得10
26秒前
科研通AI2S应助读博的小武采纳,获得10
26秒前
量子星尘发布了新的文献求助10
27秒前
最专业完成签到,获得积分10
28秒前
28秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Nuclear Fuel Behaviour under RIA Conditions 500
Sociologies et cosmopolitisme méthodologique 400
Why America Can't Retrench (And How it Might) 400
Another look at Archaeopteryx as the oldest bird 390
Optimization and Learning via Stochastic Gradient Search 300
Higher taxa of Basidiomycetes 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 催化作用 遗传学 冶金 电极 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 4676819
求助须知:如何正确求助?哪些是违规求助? 4054457
关于积分的说明 12537656
捐赠科研通 3748585
什么是DOI,文献DOI怎么找? 2070497
邀请新用户注册赠送积分活动 1099596
科研通“疑难数据库(出版商)”最低求助积分说明 979195