Spectral diagnosis and defects prediction based on ELM during the GTAW of Al alloys
Spectral diagnosis and defects prediction based on ELM during the GTAW of Al alloys
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铝合金GTAW过程中基于ELM的光谱诊断和缺陷预测
DOI：
10.1016/j.measurement.2018.12.074
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发表时间：
2019-03
期刊：
影响因子：
5.6
通讯作者：
Chen Shanben
中科院分区：
文献类型：
--
作者：
Huang Yiming;Li Shanshan;Li Jiahui;Chen Huabin;Yang Lijun;Chen Shanben
电弧光谱的定量分析对于研究电弧辐射与焊接质量之间的相互作用具有重要贡献。在本工作中，发现通过玻尔兹曼图法计算的电子温度T e 与焊接质量具有相关性，其形状分布与红外摄像机获得的结果一致。为了获得T e 曲线的平滑特征值，采用波包变换(WPT)来消除功率脉冲干扰对频谱信号的影响。研究了焊接质量与重建的 T e 曲线之间的关系。此外，还讨论了T e 变化的机制。考虑到单次温度固化的限制，为了准确描述焊缝状态，还讨论了六个光谱信号。利用上述光谱特征构建基于极限学习机（ELM）的焊接质量预测模型。所提出的方法经验证是有效的且具有较高的预测精度。
Quantitative analysis of arc spectrum makes great contribution to studying the interaction between arc radiation and welding quality. In the present work, the electron temperature T e calculated by Boltzmann plot method is found to have correlation with welding quality and its shape distribution is consistent with results obtained by the infrared video camera. To acquire smooth feature values of the T e curve, wave packet transform (WPT) is employed to eliminate the effect of power pulse interference on the spectral signal. The relationship between welding quality and reconstructed T e curve is investigated. Furthermore, the mechanism of T e variation is discussed. In consideration of the limit of the single temperature cure, six more spectral signals are discussed for describing the weld seam state accurately. The above spectral features are used to build the prediction model of welding quality based on extreme learning machine (ELM). The proposed methodologies are verified to be effective with high prediction accuracy.
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