Deep learning-based detection of aluminum casting defects and their types

Abstract

Due to its unique properties, high-pressure aluminum die-casting parts are used quite often, especially in the automotive industry. However, die-casting is a process which requires non-destructive testing of the critical components using technologies such as X-ray to examine the internal defects that are not otherwise visible. Such a timeconsuming visual inspection requires well-trained human specialists with the utmost attention. In this study, state-of-the-art deep learning-based object detection methods were trained using an X-ray image dataset of aluminum parts to detect internal defects and predict their types without human attention. The Al-Cast image dataset used in this study contains 3466 images of parts produced in high-pressure die casting machines. It is shared as an open-access original database for the nondestructive testing (NDT) community. ASTM standard definitions for aluminum casting defects are used in determining their types, and to the best of our knowledge, this novel approach is the first in the deep learning literature. Among the 12 deep learning-based object detection methods used for comparison, YOLOv5 versions yielded the highest detection accuracy (0.956 mAP) with the shortest training time (0.75 h). In addition, tests were performed for both original and contrast enhanced images on 348 test images. YOLOv5m performed an accurate detection performance of 95.9%. Additionally, YOLOv5n can process 132 images per second. This study can be considered the first step of an artificial intelligence product that can detect internal defects of aluminum casting parts with industrial standards and explain the relationship between highpressure injection die casting parameters and these defects.