脉冲熔化极气体保护焊弧长神经网络建模及参数预测
Neural Network Modeling and Parameter Prediction for Arc Length of Pulsed Gas Metal Arc Welding
关皓真 1张裕 1孙磊 1吴艳明1
作者信息
- 1. 中国船舶集团有限公司第七二五研究所,河南洛阳 471023
- 折叠
摘要
针对脉冲熔化极气体保护焊中的基值电流、脉冲电流、脉冲时间、脉冲频率和电流上升速度等5个核心脉冲参数,通过高速摄影系统采集不同参数组合下焊接过程中的电弧弧长变化,并基于60条所得试验结果建立了关于电弧长度的BP神经网络预测模型.利用该模型预测脉冲参数与焊接弧长的相关性规律,建立的弧长预测模型相关系数R2=0.91,预测误差波动范围为[-7.065 2％,7.301 0％],在单因素预测中能够较好地反映脉冲参数对弧长的影响规律和变化趋势.
Abstract
Aiming at the five core pulse parameters of pulsed gas metal arc welding,such as the base current,pulse current,pulse time,pulse frequency and current rising speed,the arc length changes during welding process under different parameter combinations are collected by using the high-speed photography system,and based on 60 experimental results,a BP neural net-work prediction model for arc length is established.The correlation coefficient R2=0.91,and the prediction error range is[-7.065 2％,7.301 0％].The BP neural network prediction model can well reflect the variation trend of arc length affected by the pulse parameters in single factor prediction.
关键词
BP神经网络/脉冲参数/P-GMAW/弧长Key words
BP neural network/pulse parameters/P-GMAW/arc length引用本文复制引用
出版年
2024
NETL
NSTL
万方数据