基于人耳听觉模型的MIG焊熔滴过渡状态识别

Droplet Transfer Modes Identification in MIG Welding Process Based on a Human Auditory Model

针对强噪声干扰下熔化极惰性气体保护焊(Melt inert-gas,MIG)的熔滴过渡状态识别问题,提出一种基于人耳听觉模型的电弧声熔滴过渡状态识别算法.采用外耳和中耳传递函数对电弧声信号进行变换,模拟耳蜗的功能对变换后的电弧声信号进行频率分解,并求分解后各频带内信号的响度功率,根据不同频带的响度功率构建电弧声的特征矢量,采用支持向量机根据获得的特征矢量实现熔滴过渡状态的识别.试验结果表明,该算法可以很好地抑制由于焊接电流的低频变化引起的电弧声低频波动,对熔滴过渡状态的识别率达到了98%以上.为了验证算法的抗噪声干扰能力,在原始信号的基础上,分别施加了不同信噪比的白噪声和环境噪声,结果表明,所提出的算法具有优良的抗噪声干扰能力,从而为焊接质量在线监控提供了一种新方法.

To identify the droplet transfer modes in a strong noise-interfered MIG welding process, a welding arc sound pattern recognition method based on human auditory model is proposed. An external and middle auditory canal transfer function is adopted to dispose the welding arc sounds and depress the low frequency noise. Through simulating the function of cochlear to decompose the disposed welding arc sound signals into different frequency bands, and the loudness in the each of frequency band is acquired through the Moore loudness model. Based on the loudness in different frequency bands a feature vector is constructed and a support vector machine model is adopted to identify the droplet transfer modes. The experimental results show that the proposed method significantly depresses the low frequency welding arc noise aroused by fluctuation of welding currents, and the correct rate for droplet transfer modes identification is higher than 98%. To verify the anti?noise interference ability of the proposed method, a series of white noise and environment noise in different signal noise ratios are added to the original welding arc sound signals. The identification results show that the proposed method has excellent anti-noise interference ability. This research provides a new method for the welding quality online monitoring.