Exploring of the defect detection in arc additive manufacturing

Arc Additive Manufacturing (AAM) is a rapidly evolving three-dimensional printing technology with great potential in manufacturing complex metal components. However, as the size and complexity of components increase, the generation of defects has become an important factor limiting its wide application. This paper provides a comprehensive discussion of the common types of defects and their causes in arc additive manufacturing, focusing on the various methods and techniques currently applied to detect these defects. From traditional Non Destructive Testing (NDT) techniques to emerging methods based on machine learning and artificial intelligence, traditional NDT techniques such as X-ray imaging and ultrasonic inspection have improved in terms of detection accuracy and speed. In contrast, emerging methods based on machine learning and artificial intelligence have demonstrated the potential to improve detection efficiency and accuracy. Nonetheless, there are still challenges to achieving comprehensive defect detection, including problems in technology integration, data acquisition, and algorithm optimization. This paper explores their potential and limitations in improving manufacturing quality and productivity.