关键词: 窄间隙焊, 电弧声, 侧壁熔合, 支持向量机, 脉冲熔化极气体保护焊

Abstract:  To grasp the internal welding status of welds in real time during welding processes, an on-line acquisition system of arc sound signals was constructed. The correlation analysis between arc sound signal characteristics and sidewall fusion states was carried out under the conditions that the torch swing center were in different positions. Arc acoustic features with strong correlation to side wall fusion state were extracted from time domain and frequency domain respectively. In order to further improve the effectiveness of the fusion state prediction, a support vector regression model for sidewall fusion state recognition was constructed by arc acoustic feature parameters. To reduce the impacts of non-features and improve the prediction accuracy of the model, genetic algorithm was used to optimize the model parameter. After parameter optimization, the recognition rate of the model is  as 93.33%, which realizes the effective recognition of the fusion states of the narrow gap sidewalls.

Key words:  , narrow gap welding, arc sound, sidewall fusion, support vector machine(SVM), pulse gas metal arc welding