Bionic design of tools in cutting: Reducing adhesion, abrasion or friction

仿生学 磨损(机械) 机械工程 粘附 过程(计算) 机制(生物学) 材料科学 制造工程 工程类 纳米技术 计算机科学 复合材料 人工智能 哲学 认识论 操作系统
作者
Haiyue Yu,Zhiwu Han,Junqiu Zhang,Shuaijun Zhang
出处
期刊:Wear [Elsevier BV]
卷期号:482-483: 203955-203955 被引量:96
标识
DOI:10.1016/j.wear.2021.203955
摘要

Cutting is defined as the process of creating an opening or a wound on a surface, particularly using a sharp tool. The process is used to promote the development including in the fields of agricultural, geological, mechanical, forestry, and medical engineering. However, cutting frequently suffers from low efficiency, poor quality, and high energy consumption because of adhesion, abrasion, or friction occurring on the surface of the cutting tool. To solve these problems, bionics has been introduced into the design of tools by several scholars. However, a comprehensive review of the bionic design of cutting tools is not yet available. To address this gap, a literature survey of bionic applications in cutting tool design is conducted in this study. To explain the mechanisms of different bionic tools systematically, the review is conducted from the perspective of cutting materials in different engineering fields because of their different mechanisms of adhesion, abrasion, or friction. Four types of bionic cutting tools are summarized and analyzed based on existing literature: soil, rock, metal, and biological tissue cutting tools. Future development trends of bionic tools from the perspectives of their bionic mechanism and applications are discussed based on literature analysis and bionic investigations. It is expected that new inspirations will be drawn by researchers and the development of bionic cutting tools will be promoted based on this study. • Studies about bionic cutting tools are reviewed. • Bionic cutting tools from multiple engineering are summarized and analyzed. • Bionic mechanisms to inhibit cutting tools' adhesion, abrasion or friction are shown. • Research prospects of bionic cutting tools are proposed.
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
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
1秒前
2秒前
2秒前
Chen发布了新的文献求助10
2秒前
Rainor完成签到,获得积分10
3秒前
4秒前
李健应助朱慈烺采纳,获得10
7秒前
Ava应助执着冷风采纳,获得10
8秒前
8秒前
负责月光发布了新的文献求助10
9秒前
goffe发布了新的文献求助10
9秒前
JamesPei应助Chen采纳,获得30
10秒前
George发布了新的文献求助10
11秒前
顺心如风发布了新的文献求助10
12秒前
14秒前
15秒前
大何发布了新的文献求助10
16秒前
123发布了新的文献求助10
16秒前
情怀应助lucky采纳,获得10
16秒前
FashionBoy应助llllllll采纳,获得10
17秒前
七友完成签到,获得积分10
18秒前
卫子善发布了新的文献求助20
19秒前
顺心的凌萱完成签到,获得积分10
19秒前
风清扬发布了新的文献求助10
19秒前
科研通AI6.4应助niufuking采纳,获得10
20秒前
ME发布了新的文献求助10
20秒前
Akim应助sadascaqwqw采纳,获得10
20秒前
21秒前
上官若男应助hxc采纳,获得20
22秒前
Anna发布了新的文献求助20
23秒前
23秒前
微风完成签到 ,获得积分10
24秒前
摆烂小子发布了新的文献求助10
26秒前
明尘发布了新的文献求助10
27秒前
你好吗发布了新的文献求助10
27秒前
Owen应助风清扬采纳,获得10
28秒前
豆芽完成签到,获得积分10
28秒前
29秒前
搞怪的语堂完成签到,获得积分10
29秒前
32秒前
高分求助中
卤化钙钛矿人工突触的研究 2000
Malcolm Fraser : a biography 700
Signals, Systems, and Signal Processing 610
Software that combines deep learning,3D reconstruction and CFD to analyze the state of carotid arteries from ultrasound imaging 500
Bounds for Statistical Estimation in Semiparametric Models 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 500
Ideology and Meaning-Making under the Putin Regime 450
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6494156
求助须知:如何正确求助?哪些是违规求助? 8291371
关于积分的说明 17693143
捐赠科研通 5586880
什么是DOI,文献DOI怎么找? 2916043
邀请新用户注册赠送积分活动 1893050
关于科研通互助平台的介绍 1751696