基于粗糙集的窄间隙MAG焊焊缝偏差建模方法

Modeling method for narrow gap MAG welding seam tracking based on rough sets

针对旋转电弧窄间隙MAG多层单道焊焊缝跟踪需要,提出一种基于粗糙集的焊缝偏差智能建模方法.首先设计工件模仿多层单道焊的坡口状态,并采集不同偏差下的试验数据,然后根据电弧长度变化的Matlab仿真和试验数据变化规律将一个旋转周期内的电流划分为12个区间,提取各区间电流平均值和左右区间差值来建立决策表.通过对决策表离散化、约简,最终获得"IF…THEN"形式的知识模型.结果表明,对模型验证并与神经网络对比,二者预测能力相当,精度满足焊缝跟踪需要,而粗糙集模型的可理解性大大提高且能进一步从数据中发掘焊缝偏差与焊接电信号潜在的规律,为电弧控制器设计做准备.

For welding seam tracking of multi-layer single pass welding by narrow gap rotating arc MAG,an intelligent modeling method based on rough sets(RS) theory for seam deviation was put forward.First,workpiece was designed and processed to mimic multi-layer single pass welding groove,and enough experimental data were acquired under different deviations.Second,according to the simulation results of arc length and variation of experimental data,current signal was divided into 12 intervals in each rotating cycle.The mean value of each interval and differences between left and right intervals were computed to build decision table.Then after discretization and reduction for decision table,the knowledge model of'IF…THEN' form was obtained.At last the model was validated and compared with BP net model.It showed that the both models had similar predictive capability,and RS model precision could meet actual needs.Furthermore,RS model had better comprehensibility,and was useful to find potential laws between seam deviations and welding electrical signals from experimental data.The research was helpful for controller design.