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| 铝合金回填式搅拌摩擦点焊缺陷的超声检测及信号识别 |
| Ultrasonic Testing and Signal Identification of the Defects in Aluminium Alloy Refill Friction Stir Spot Welding |
| Received:August 31, 2019 Revised:November 10, 2019 |
| DOI：10.3969/j.issn.1674-6457.2019.06.011 |
| 中文关键词: 回填式搅拌摩擦点焊 超声检测 时频变换 小波分析 |
| 英文关键词: RFSSW ultrasonic C-scan time-frequency transform wavelet analysis |
| 基金项目: |
| Author Name | Affiliation | | SUN Jing-ru | 1. Shaanxi Key Laboratory of Friction Welding Technologies, State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China | | LI Jing-long | 1. Shaanxi Key Laboratory of Friction Welding Technologies, State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China | | DOU Jian-xin | 1. Shaanxi Key Laboratory of Friction Welding Technologies, State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
2. AVIC Manufacture Technology Institute, Beijing 100024, China | | XIONG Jiang-tao | 1. Shaanxi Key Laboratory of Friction Welding Technologies, State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China | | SHI Jun-miao | 1. Shaanxi Key Laboratory of Friction Welding Technologies, State Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China | | ZHAO Hua-xia | 2. AVIC Manufacture Technology Institute, Beijing 100024, China |
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| 中文摘要: |
| 目的 利用水浸聚焦超声C扫描对2524铝合金回填式搅拌摩擦点焊（RFSSW）接头进行检测并完成缺陷识别。方法 以A扫描时域信号为对象，绘制能量谱与金相作对照，确定焊合区；分析未焊合、聚集型孔洞及弥散型孔洞的时域和频域波形特征，并对频域信号进行小波分析，提取特征量。结果 缺陷区反射回波的归一化能量不低于0.02；不同形式的缺陷（聚集型孔洞、弥散型孔洞、弱连接）其频谱形状特征不同，具有不同的峰值个数。结论 通过金相验证，表明超声波C扫描对接头缺陷可进行有效的检测和区分。 |
| 英文摘要: |
| The paper aims to conduct immersion ultrasonic C-scan test for identifying different defects of the 2524 aluminum alloy joints with refill friction stir spot welding (RFSSW). With A-scan signal as the object, the energy spectrum was compared with the microstructures to determine the welded area. The time-domain and frequency-domain waveform characteristics of the unwelded, aggregated and dispersed holes were analyzed. The frequency-domain signal was analyzed by wavelet analysis to extract the feature. The normalized energy of reflection echo in the defect zone was not less than 0.02; different defects (aggregated, dispersed holes and kissing bond) had different frequency spectrum features and different peaks. The result of metallographic verification demonstrates that ultrasonic C-scan can effectively detect and distinguish defects of the joints. |
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