TFM imaging of aeroengine casing ring forgings with curved surfaces using acoustic field threshold segmentation and vector coherence factor

The aeroengine casing ring forgings have complex cross-section shapes,when the conventional ultrasonic or phased array is applied to detect such curved surfaces,the inspection images always have low resolution and even artifacts due to the distortion of the wave beam.In this article,taking a type of aeroengine casing ring forging as an example,the Total Focusing Method(TFM)algorithms for curved surfaces are investigated.First,the Acoustic Field Threshold Segmentation(AFTS)algorithm is proposed to reduce background noise and data calculation.Furthermore,the Vector Coherence Factor(VCF)is adopted to improve the lateral resolution of the TFM imaging.Finally,a series of 0.8 mm diameter Side-Drilled Holes(SDHs)are machined below convex and concave surfaces of the specimen.The quantitative comparison of the detection images using the conventional TFM,AFTS-TFM,VCF-TFM,and AFTS-VCF-TFM is implemented in terms of data volume,imaging Signal-to-Noise Ratio(SNR),and defect echo width.The results show that compared with conventional TFM,the data volume of AFTS-VCF-TFM algorithm for convex and concave is decreased by 32.39%and 73.40%,respectively.Moreover,the average SNR of the AFTS-VCF-TFM is gained up to 40.0 dB,while the average 6 dB-drop echo width of defects is reduced to 0.74 mm.

A Modified Block Matching Motion Estimation Algorithm for the Very Low Bit-Rate video Coding

Block matching motion estimation techniques have been widely used in video coding applications. However, they also show their deficiency in the coherence of motion vectors and antinoise ability. This paper proposes a modified algorithm which can adopt any one of existing search algorithms and pays more attention to the correlation of neighboring blocks.It will be shown that the proposed algorithm is simple and significantly reduces the computational complexity. Simulation results also show that this algorithm improves the smoothness of the motion field, hence reducing the cost to code the motion vectors while keeping good performance comparable with the conventional block matching motion estimation algorithm.