Online monitoring of dissimilar material FSW based on SSTFT and KSVD

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.