Texture Control for Improving Deep Drawability in Rolled and Annealed Aluminum Alloy Sheets

材料科学 再结晶(地质) 合金 退火(玻璃) 冶金 拉深 限制 纹理(宇宙学) 表层 复合材料 图层(电子) 人工智能 机械工程 古生物学 工程类 生物 计算机科学 图像(数学)
作者
Hirofumi Inoue,Takayuki Takasugi
出处
期刊:Materials transactions [The Japan Institute of Metals]
卷期号:48 (8): 2014-2022 被引量:88
标识
DOI:10.2320/matertrans.l-mra2007871
摘要

In order to find a possibility of texture control for improving deep drawability in rolled and annealed aluminum alloys, the relation among recrystallization texture, r-value and limiting drawing ratio was examined for sheet materials with various textures. By using specimens with {111} texture prepared artificially, limiting drawing ratio could be measured in a wide range of average r-value from 0.4 to 1.6. Experimental results demonstrated that there was a positive correlation between average r-value and limiting drawing ratio even in aluminum alloys. This means that an increase in average r-value leads to improvement of deep drawability. Warm rolling that forms shear texture including {111} components, therefore, was conducted to enhance average r-value for Al-Mg and Al-Mg-Si alloys. Recrystallization texture of an annealed Al-Mg alloy consisted of retained shear texture components in the surface layer and cube plus R orientations in the center layer. The average r-value was considerably improved compared with that of a cold rolled sheet. On the other hand, a T4-treated Al-Mg-Si alloy had a relatively weak cube texture on the whole, though the surface layer showed a different texture from the center. In this case, warm rolling was ineffective in improving average r-value, in spite of the existence of surface texture with higher r-value. However, the relation between recrystallization texture and experimental r-value was successfully explained for the Al-Mg-Si alloy as well as for the Al-Mg alloy, based on r-value calculations from overall texture through sheet thickness.

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
新风完成签到,获得积分10
刚刚
wanci应助小美最棒采纳,获得10
刚刚
1秒前
害羞的夏柳完成签到,获得积分10
1秒前
Mine_cherry完成签到 ,获得积分10
1秒前
freebird完成签到,获得积分10
1秒前
6S6完成签到,获得积分10
1秒前
孟芷旭孟芷旭完成签到 ,获得积分10
2秒前
迟早是个小富婆完成签到,获得积分10
2秒前
CodeCraft应助wuxunxun2015采纳,获得10
3秒前
3秒前
jasmime完成签到,获得积分10
3秒前
慕青应助美满的红酒采纳,获得10
3秒前
英俊的铭应助四然采纳,获得30
4秒前
劉紹慶完成签到 ,获得积分10
4秒前
开放鸿涛应助xtt采纳,获得10
4秒前
大模型应助无限妙梦采纳,获得10
4秒前
weixin112233完成签到,获得积分10
4秒前
5秒前
大胆笑翠完成签到,获得积分10
5秒前
诗谙完成签到,获得积分10
5秒前
充电宝应助三块石头采纳,获得10
6秒前
天天发布了新的文献求助10
6秒前
6秒前
Ll完成签到,获得积分20
6秒前
6秒前
6秒前
华仔应助星燃采纳,获得10
6秒前
7秒前
紧张的金毛完成签到,获得积分10
7秒前
鸭梨完成签到,获得积分10
7秒前
7秒前
量子星尘发布了新的文献求助10
7秒前
蜘蛛道理完成签到 ,获得积分10
7秒前
阿猫发布了新的文献求助10
7秒前
8秒前
在水一方应助星星人采纳,获得10
8秒前
murphy完成签到,获得积分10
9秒前
kingwill举报自觉的语海求助涉嫌违规
9秒前
9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
Basic And Clinical Science Course 2025-2026 3000
人脑智能与人工智能 1000
花の香りの秘密―遺伝子情報から機能性まで 800
Process Plant Design for Chemical Engineers 400
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
Signals, Systems, and Signal Processing 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5612427
求助须知:如何正确求助?哪些是违规求助? 4696552
关于积分的说明 14893385
捐赠科研通 4733235
什么是DOI,文献DOI怎么找? 2546401
邀请新用户注册赠送积分活动 1510561
关于科研通互助平台的介绍 1473423