基于SSTFT与KSVD的异种材料FSW在线监测  

Online monitoring of dissimilar material FSW based on SSTFT and KSVD

作　　者：龙海威 张佳莹[1] 刘锐 孙屹博[1] 魏晓 杨鑫华 LONG Haiwei;ZHANG Jiaying;LIU Rui;SUN Yibo;WEI Xiao;YANG Xinhua(College of China Railway Rolling Stock Corporation,Dalian Jiaotong University,Dalian,116028,China;State Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology,Baotou,014032,China;Dalian Key Laboratory of Welded Structures and Intelligent Manufacturing Technology of Rail Transportation Equipment,Dalian,116028,China)

机构地区：[1]大连交通大学,詹天佑学院,大连116028 [2]特种车辆设计与制造集成技术国家重点实验室,包头014032 [3]辽宁省轨道交通装备焊接与可靠性重点实验室,大连116028

出　　处：《焊接学报》2024年第11期77-84,共8页Transactions of The China Welding Institution

基　　金：国家自然科学基金资助项目(52005071);特种车辆设计与制造集成技术国家重点实验室(GZ2023KF015);辽宁省自然科学基金(2022-MS-342)。

摘　　要：异种材料轻量化结构是航空航天、铁路、汽车等领域的关键技术和研究热点之一,搅拌摩擦焊(FSW)是连接异种材料的有效方法,由于异种材料物理和化学性质的不同,容易在焊接过程中产生缺陷.针对铝合金与碳纤维增强热塑性塑料(CFRTP)搅拌摩擦焊(FSW)缺陷监测提出了基于同步压缩短时傅立叶变换与K-奇异值分解(SSTFT-KSVD)在线监测方法.使用声发射(AE)信号实时监测FSW状态,利用同步压缩短时傅立叶变换(SSTFT)提取时频域特征,最后通过K-奇异值分解(KSVD)模型对焊接状态与焊接缺陷进行了分类.结果表明,AE信号频率成分集中在10 kHz,17 kHz,23 kHz和25 kHz 4个频段,熔核塌陷和表面擦伤缺陷发生时,23 kHz频段的功率分别转移到10 kHz,而表面擦伤发生时,25 kHz频段的功率转移到17 kHz.在缺陷预测方面,KSVD预测模型的平均准确率达到90%,响应时间达到10 ms量级,比神经网络快100倍.基于SSTFT-KSVD在线监测方法可以实现对Al-CFRTP异种材料的FSW快速监测.Lightweight is one of the key technologies and research hotspots in the fields of aerospace,railway,and vehicles,which makes the demand for the joining of dissimilar materials such as Al-based and carbon fiber-based materials.Friction stir welding(FSW)is a potential method for the joining of two kinds of materials,however,defects are easily generated due to different physical and chemical properties.In this paper,an in-situ monitoring and defect diagnosis method for FSW of 6061-T6 Al alloy and Continuous Fiber reinforced thermoplastic plastics(Al-CFRTP)is proposed.In this method,the acoustic emission(AE)signal at the upper workpiece of the lap joint is obtained.Time-frequency domain features are extracted by synchronous compression short-time Fourier transform(SSTFT).The welding status is classified by the K-singular value decomposition(KSVD),including pin tool pressure and down,nugget collapse defects,flash defects,and surface galling defects.The Al-CFRTP FSW experiments are carried out based on this method.From the SSTFT results,the dominant frequency of AE signals is concentrated in four main frequency bands of 10 kHz,17 kHz,23 kHz,and 25 kHz.The frequency band shifts from 23 kHz to 10 kHz accompanied by the nugget collapse defects and shifts from 25 kHz to 17 kHz accompanied by the flash and sueface galling defects.Results indicate that the recognition accuracy of defects reaches 90%based on KSVD prediction model.The computation speed is 100 times faster than neural networks with nearly the same recognition precision.The SSTFT-KSVD method is effective for In-situ monitoring and defect diagnosis of the FSW process.

关 键 词：异种材料搅拌摩擦焊 声发射信号 同步压缩短时傅立叶变换 K-奇异值分解 在线监测 

分 类 号：TG441.7[金属学及工艺—焊接]