已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Development a new methodology for measuring deep drawing forces based on dimensionless evaluation

作者
Saeed Hajiahmadi,Majid Elyasi,Mohsen Shakeri
出处
期刊:Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science [SAGE Publishing]
卷期号:235 (19): 4057-4069 被引量:6
标识
DOI:10.1177/0954406220969718
摘要

In this research work, dimensionless models based on geometric parameters have been developed for the deep drawing process of rectangular cups to reduce the manufacturing costs on a large scale of application in a noticeable way. In the following, geometric parameters were given in dimensionless form by the Π-Buckingham dimensional analysis method and a series of dimensionless groups were found for both circular and rectangular initial blank. To find the best group of dimensionless geometric parameters, different cup scales 1:1, 2:1, 4:1 and 5:1 are evaluated numerically by ABAQUS Finite Element (FE) software, validated by experimental work. After all effective geometric parameters have been analyzed, the best fitting relational model of dimensionless parameters is found for rectangular and circular blank separately. Various thicknesses of St12 sheet metals were used for experimental validation, which were formed at room temperature. Also, results and response parameters were compared in the simulation process, experimental tests and dimensionless models. By looking at the outcomes, it is demonstrated that the geometric qualities of a large scale can be predicted by a small scale, utilizing the proposed dimensionless model. A comparison of the outcomes for dimensionless models and experimental tests shows that proposed dimensionless models have a high degree of precision in determining geometrical parameters and the prediction of drawing force. Furthermore, the dimensionless analysis was generalized to ensure high precision estimation of geometric values for large geometric scales.

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
科研通AI6.4应助e麓绝尘采纳,获得10
刚刚
我抑郁完成签到 ,获得积分20
4秒前
Joy完成签到 ,获得积分10
5秒前
yqt完成签到,获得积分10
6秒前
顾矜应助加湿器采纳,获得10
7秒前
骑猪看月完成签到,获得积分10
11秒前
科研通AI6.4应助e麓绝尘采纳,获得10
11秒前
桐夜完成签到 ,获得积分10
13秒前
迷路月光完成签到,获得积分10
14秒前
义气幼珊完成签到 ,获得积分10
14秒前
15秒前
Jonathan完成签到,获得积分10
16秒前
眼睛大的凡波完成签到,获得积分10
17秒前
我抑郁关注了科研通微信公众号
18秒前
Akim应助搞怪的砖家采纳,获得10
18秒前
Jonathan发布了新的文献求助10
18秒前
时尚面包完成签到 ,获得积分10
18秒前
Hasson完成签到,获得积分10
22秒前
天天快乐应助orange采纳,获得30
23秒前
24秒前
CodeCraft应助林超采纳,获得10
26秒前
Ru完成签到 ,获得积分10
27秒前
Boro发布了新的文献求助10
29秒前
Huang完成签到 ,获得积分10
31秒前
32秒前
33秒前
33秒前
戴鹿角王冠的拉斯特完成签到,获得积分10
34秒前
34秒前
小苏完成签到,获得积分10
37秒前
科研通AI6.2应助麦子采纳,获得10
37秒前
CYL07完成签到 ,获得积分10
38秒前
doge发布了新的文献求助10
39秒前
you发布了新的文献求助10
40秒前
hzw83发布了新的文献求助10
40秒前
40秒前
青云完成签到,获得积分10
41秒前
吃不完的玉米完成签到,获得积分10
43秒前
111完成签到 ,获得积分10
43秒前
46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
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
核安全综合知识2024版 500
Photothermal Science and Techniques 500
Digital Displacement Hydrostatic Transmission for Rotorcraft and Distributed Propulsion 500
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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7711077
求助须知:如何正确求助?哪些是违规求助? 9267533
关于积分的说明 20066997
捐赠科研通 7287477
什么是DOI,文献DOI怎么找? 3297183
关于科研通互助平台的介绍 2451621
邀请新用户注册赠送积分活动 2304182