Titanium alloy has the characteristics of high strength

good corrosion resistance and high heat resistance

V and is widely used in aerospace and other fields. Aiming at the problems such as low signal-to-noise ratio

easy omission during ultrasonic phased array detection of internal micro defects

a deep learning based ultrasonic phased array detection image noise reduction method for micro defects was proposed. Firstly

the original images with defects and noise are obtained through the phased array detection experiments of titanium alloy test block

and the Mask RCNN model is trained to construct high–low noise data sets. Then

the noise reduction model of micro defects detection images is designed based on the variational autoencoder. By comparing with the traditional noise reduction algorithms

it is proved that the proposed algorithm can retain the defect details of the original image. Compared with the original image with noise

the peak signalto-noise ratio is optimized by 11.35% and the structural similarity is improved by 154.17%. Finally

the ultrasonic phased array testing experiment of a titanium alloy aviation casing ring was carried out. The proposed method was used to reduce the noise of the image with a φ 0.2 mm flat bottom hole inside the ring

effectively reducing the influence of scattered noise on the detection of small defects

it’s also proved that the proposed noise reduction algorithm has good generalization performance.