Joint strength prediction in a pulsed MIG welding process using hybrid neuro ant colony-optimized model

被引：0
|
作者：
N. Raghavendra
Rakshit Koranne
Sukhomay Pal
Surjya K. Pal
Arun K. Samantaray
机构：
[1] Indian Institute of Technology,Department of Mechanical Engineering
来源：
The International Journal of Advanced Manufacturing Technology | 2009年 / 41卷
关键词：
Back-propagation neural network; Ant colony optimization; PMIGW; Weld strength;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this work, a pulsed metal inert gas welding (PMIGW) process is modeled by using a hybrid soft computing technique. Ant colony optimization (ACO) and back-propagation neural network (BPNN) models are combined to predict the ultimate tensile strength of butt-welded joints. A large number of experiments have been conducted, and comparative study shows that the hybrid neuro ant colony-optimized model produces faster and also better weld-joint strength prediction than the conventional back propagation model.
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页码：694 / 705
页数：11
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