Porosity Evaluation of Additively Manufactured Components Using Deep Learning-based Ultrasonic Nondestructive Testing

Abstract

This study proposed deep learning-based ultrasonic nondestructive testing for porosity evaluation of additively manufactured components. First, porosity mechanisms according to additive manufacturing (AM) processing conditions were studied using traditional scanning acoustic microscopy and optical microscopy. Second, correlations between ultrasonic properties and porosity content were analyzed. The correlation results showed that the increased porosity content resulted in a decreased ultrasonic velocity and increased ultrasonic attenuation coefficient. Third, various levels of porosities were evaluated using a deep learning model based on a fully connected deep neural network that was trained on raw ultrasonic signals measured in the AM samples. After training, the testing performance of the trained model was evaluated. Additionally, the generalization performance of the pre-trained model was assessed using newly fabricated AM samples that were not used for training. The results showed that the porosity content evaluated by the pre-trained model matched well with that measured via traditional scanning acoustic microscopy, thus demonstrating the feasibility of deep learning-based ultrasonic nondestructive testing for porosity evaluation of additively manufactured components.