旋转电弧传感器焊枪偏差信息识别方法

焊枪偏差信息识别是实现焊缝跟踪的必要条件,考虑到焊接电流信号易受外界噪声的干扰,采用软阈值小波滤波方法对旋转电弧传感器采集到的电流信号进行滤波处理,使电流波形得到了明显的改善,提高了电流信号的信噪比.为了更加准确地得到焊枪偏离焊缝的信息,提出了模糊聚类和神经网络焊枪偏差识别算法,并且分别采用左右积分法、特征谐波法、模糊聚类法和神经网络法进行焊枪偏差识别,最后对四种识别方法检测到的偏差进行融合,获得焊枪偏离焊缝的信息.结果表明,该方法可以提高偏差识别的精确度和可靠性.

Identification of welding torch deviation with rotating arc sensor

Identification of welding torch deviation is necessary to accomplish welding seam tracking.In consideration of the fact that welding current signals are often disturbed by outside noises, soft threshold wavelet filtering method is applied to process welding current signals, which makes the welding current shape obviously smooth and the signal-to-noise ratio much be improved.For more exactly acquiring the distance of welding torch depart from welding seam, fuzzy clustering and artificial neural network deviation identifying methods were proposed.Left-right integral method, character harmonic method, fuzzy clustering method and artificial neural network method were used to identify the welding torch deviation, respectively.In the last, the deviations acquired by the four methods were fused and the last deviation was obtained.The experiment results show that the fusion method can greatly improve the precision and reliability of identifying welding torch deviation.