Audible Sound-Based Intelligent Evaluation for Aluminum Alloy in Robotic Pulsed GTAW: Mechanism, Feature Selection, and Defect Detection
Audible Sound-Based Intelligent Evaluation for Aluminum Alloy in Robotic Pulsed GTAW: Mechanism, Feature Selection, and Defect Detection
复制标题
机器人脉冲 GTAW 中铝合金的基于声音的智能评估：机制、特征选择和缺陷检测
DOI：
10.1109/tii.2017.2775218
复制
发表时间：
2018-07
影响因子：
12.3
通讯作者：
Chen Shanben
中科院分区：
文献类型：
--
作者：
Zhang Zhifen;Wen Guangrui;Chen Shanben
铝合金是航空航天工业的主要结构材料。脉冲气体保护钨极电弧焊中铝合金的在线缺陷检测仍然具有挑战性，特别是随着机器人技术应用的不断增加。提出了一种基于电弧可听声感知的机器人脉冲电弧电弧焊接中铝合金焊透缺陷实时评估的智能方法。采用相关分析、高速摄像机观测、频谱分析等方法对电弧声的产生机理进行了研究。提出了基于Fisher距离和主成分分析（PCA）的两种特征选择方法来选择与焊缝缺陷相关的频率分量，并对其性能进行定性和定量分析。最后，建立了支持向量机与网格搜索优化和交叉验证相结合的分类模型（SVM-GSCV），用于识别欠穿透、正常穿透和烧穿。结果表明，该方法具有较高的精度和鲁棒性。本文对增材制造或加工工业的状态监测具有一定的指导意义。
Aluminum alloy is the main structure material in aerospace industry. Online defect detection for aluminum alloy in pulsed gas tungsten arc welding (GTAW) is still challenging, especially for increasing application of robotics. This paper presents an intelligent methodology for real-time evaluation of weld penetration defects based on arc audible sound sensing for aluminum alloy in robotic-pulsed GTAW. The generation mechanism of arc sound was investigated using correlation analysis, high-speed camera observing and frequency spectrum analysis before denoising of arc sound. Two feature selection approaches based on Fisher distance and principal component analysis (PCA) were developed to select the frequency components related to seam defects, and then, their performance were qualitatively and quantitatively analyzed. Finally, a new classification model integrating support vector machine with grid search optimization and cross-validation (SVM-GSCV) was established to identify underpenetration, normal penetration, and burning through. The proposed methodologies were verified to be effective with high accuracy and robustness. This paper can provide some guidance for condition monitoring of additive manufacturing (AM) or process industry.
登录
查看更多内容
DOI：
10.5772/49987
发表时间：
2012-11
期刊：
--
影响因子：
--
作者：
M. Węglowski
通讯作者：
M. Węglowski
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
Weihong Guo
通讯作者：
Weihong Guo
DOI：
10.1016/j.advengsoft.2015.02.001
发表时间：
2015-07
期刊：
Adv. Eng. Softw.
影响因子：
--
作者：
U. Kumar;Inderjeet Yadav;S. Kumari;K. Kumari;Nitin Ranjan;R. K. Kesharwani;R. Jain;Sachin Kumar-Sachin-Ku
通讯作者：
U. Kumar;Inderjeet Yadav;S. Kumari;K. Kumari;Nitin Ranjan;R. K. Kesharwani;R. Jain;Sachin Kumar-Sachin-Ku
DOI：
10.1109/tmech.2014.2363050
发表时间：
2015-06
期刊：
IEEE/ASME Transactions on Mechatronics
影响因子：
--
作者：
Yukang Liu;Yuming Zhang
通讯作者：
Yukang Liu;Yuming Zhang
影响因子：
1.6
作者：
Jing Wang;Bo Chen;Huabin Chen;Shanben Chen
通讯作者：
Jing Wang;Bo Chen;Huabin Chen;Shanben Chen