Detection of Traceability Features Embedded in Metal Additive Manufacturing Components by Phased Array Ultrasonic Testing

Abstract

With the continuous advancement of additive manufacturing (AM) processes, ensuring that traceability and security for AM components has become paramount. Embedding unique identification features in AM components, akin to fingerprints, is essential for logistics management, certification, and counterfeiting prevention. In this article, we propose a novel approach utilizing quick response (QR) codes embedded via arrangements of unmelted features in rectangular, cylindrical, and spherical shapes within steel blocks (MPIF 4406) fabricated using laser powder bed fusion (LPBF). While computed tomography (CT) has been the dominant method for reading embedded QR codes, this article utilizes high-frequency phased array ultrasonic testing (PAUT) for reading these QR codes for the first time. Due to the layer-by-layer manufacturing process, the up-facing printed surfaces of the QR codes exhibit smooth characteristics (upskin), while the down-facing surfaces are rough (downskin). Ultrasound images from both surfaces are captured, each yielding distinct results. These captured images undergo image processing to compare them with their original designs. Linear and nonlinear image processing filters are applied to enhance the captured images, followed by feature extraction using two methods, Residual Network-50 (ResNet-50) and Histogram of Oriented Gradients (HOG), to evaluate their similarity to the original QR codes. The results reveal similarity percentages ranging from 70% to 85%. Most QR code images are readable, with upskin ultrasonic data providing better readability. This research underscores high-frequency PAUT as a promising solution for the rapid scanning of embedded QR codes in metal AM components, showcasing its potential for enhancing traceability and security in AM processes.