Ultrasonic online monitoring method of internal defects inmetal additive manufacturing

Metal additive manufacturing (AM) technology enables the direct fabrication of complex components. However, the formation of internal defects remains a significant challenge, limiting the consistent production of high-quality key parts. Online monitoring of internal defect formation during the manufacturing process is critical for ensuring quality control and enhancing the practical application of AM products. Laser ultrasound has demonstrated significant potential for rapid defect scanning and detection, owing to its non-contact and multimodal capabilities. Additionally, the layer-by-layer accumulation process characteristic of additive manufacturing (AM) provides a flat detection surface during the printing of each layer, effectively addressing the challenges posed by the limited detection space in complex components. Compared to other non-destructive testing (NDT) methods, ultrasonic testing (UT) further enables the correlation of online monitoring data with offline inspection results. However, this approach faces certain limitations. The rough surfaces and complex geometries of additive samples lead to high attenuation, low signal-to-noise ratio (SNR) in ultrasonic signals, and interference from structural waves. Under these conditions, extracting effective defect signals and visually displaying internal defects remain challenging. We propose an ultrasonic online monitoring method that employs a bottom piezoelectric sensor to generate excitation signals and constructs a scanning array on the surface using a laser Doppler vibrometer. This approach enables the timely acquisition of effective ultrasonic signals across varying printing layer thicknesses during the additive manufacturing process while mitigating interference from structural waves. To address the challenges of high ultrasonic attenuation and low signal-to-noise ratio (SNR) caused by the complex geometries of additive samples, the interaction between ultrasound and defects was simulated using the finite element method. Defect characteristic signals were identified and integrated into a proposed signal classification and focusing imaging algorithm, facilitating defect signal extraction and reconstruction.