Optimization of laser spiral welding using Response surface methodology and genetic algorithms

焊接 响应面法 镀锌 材料科学 遗传算法 螺旋(铁路) 激光功率缩放 过程(计算) 功率(物理) 人工神经网络 激光束焊接 极限抗拉强度 实验设计 机械工程 结构工程 算法 激光器 计算机科学 数学 复合材料 工程类 光学 人工智能 机器学习 物理 统计 图层(电子) 操作系统 量子力学
作者
Bin Zhou,Jieshi Chen,Yang Zhang,Shanglei Yang,Hao Lü
出处
期刊:Journal of Intelligent and Fuzzy Systems [IOS Press]
卷期号:45 (2): 2381-2392
标识
DOI:10.3233/jifs-224448
摘要

In the laser spiral welding (LSW) process, the welding parameters have a significant impact on the weld quality. In this paper, experiments were conducted and experimental data were collected on galvanized steel sheets using the LSW process, and mathematical models were developed using response surface methodology (RSM) and genetic algorithm (GA) to verify the specific effects of each process parameter on the weld and to perform process optimization. Laser power, welding speed, gap and focal length were selected as the influencing factors, and melt depth, melt width and concave as the output results. In the RSM model we found that the laser power was positively correlated with the weld depth and width, while the welding speed was inversely correlated with the weld depth and width, the gap was positively correlated with the amount of concave, and the focal length had no significant effect on the weld. In the GA model we use a large amount of experimental data for BP neural network training and iterative optimization using a genetic algorithm. Validation experiments were conducted on two models, and the results indicated that the two models had higher accuracy in predicting the welding depth and width compared to predicting the concave. The GA model had an 8% increase in tensile strength and a 25% increase in plasticity of the weld joint obtained from the optimal process compared to the RSM model. The GA model has higher accuracy in optimizing the LSW process.
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
稳重的八宝粥完成签到 ,获得积分20
刚刚
adgcxvjj应助wxyllxx采纳,获得10
刚刚
Hollow完成签到,获得积分10
刚刚
刚刚
1秒前
何三岁发布了新的文献求助10
1秒前
1秒前
天天快乐应助cliu6572采纳,获得10
1秒前
SYLH应助尛瞐慶成采纳,获得10
1秒前
3秒前
听海发布了新的文献求助10
4秒前
4秒前
AOtaku发布了新的文献求助10
5秒前
5秒前
wu完成签到,获得积分10
5秒前
5秒前
7秒前
7秒前
一颗苹果发布了新的文献求助30
8秒前
Hello应助认真的豌豆采纳,获得10
8秒前
adgcxvjj应助韶安萱采纳,获得10
10秒前
10秒前
11秒前
FashionBoy应助苏楠采纳,获得10
11秒前
爆米花应助玛卡巴卡采纳,获得10
11秒前
ZZZ发布了新的文献求助10
12秒前
13秒前
狼啸天应助Richard采纳,获得10
13秒前
善学以致用应助AOtaku采纳,获得10
13秒前
Owen应助小马采纳,获得10
14秒前
听海发布了新的文献求助10
14秒前
高金龙完成签到 ,获得积分10
14秒前
科研通AI5应助bjbmtxy采纳,获得30
15秒前
李伟发布了新的文献求助10
15秒前
六六大顺完成签到,获得积分10
15秒前
小白完成签到 ,获得积分10
15秒前
所所应助zed采纳,获得10
15秒前
adgcxvjj应助wxyllxx采纳,获得10
17秒前
17秒前
Re完成签到,获得积分10
17秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 720
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Typology of Conditional Constructions 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 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 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3565922
求助须知:如何正确求助?哪些是违规求助? 3138683
关于积分的说明 9428454
捐赠科研通 2839408
什么是DOI,文献DOI怎么找? 1560695
邀请新用户注册赠送积分活动 729854
科研通“疑难数据库(出版商)”最低求助积分说明 717669