Classification of Ultrasonic Signs Pre-processed by Fourier Transform through Artificial Neural Network Using the Echo Pulse Technique for the Identification of Defects in Welded Joints of Structural Steel

It is due to the need to ensure the security and integrity of equipment, that the non-destructive tests have been increasingly used in the industrial sector. Among these, the ultrasonic pulse echo technique is the most used in industry, mainly for its simplicity and efficiency. With one transducer only, it is possible to emit the ultrasonic and receive the echo pulse. The ANNs (artificial neural networks) are artificial intelligence techniques that, when properly trained, align themselves to inspection tests becoming a powerful tool in the detection and fault identification. In this work, the echo pulse technique was used to detect discontinuities in welds, where ANNs were fed from the information obtained by digital signal processing techniques (Fourier transform), to identify and classify three distinct classes of defects. Results showed that with the combination of feature extraction by Fourier transformation and classification with neural networks, it is possible to obtain an automatic defect detection system in welded joints with average efficiency.