Micro X-ray Computed Tomography and Machine Learning Assessment of Impregnation Efficacy of Die-Casting Defects in Metal Alloys

Die-cast light metal alloys in various industrial applications require precise airtightness, and vacuum pressure impregnation (VPI) is typically used to seal casting defects to ensure product reliability. Evaluating the efficacy of VPI in sealing alloy defects is crucial. In this study, laboratory-based micro X-ray computed tomography (micro-XCT) was effectively employed in conjunction with advanced direct conversion CdTe semiconductor sensors to nondestructively evaluate the efficacy of standard VPI in sealing die-casting defects of industrial Al alloys. The internal casting defects and the low-atomic-number impregnation sealant distribution were visualized by adjusting the scalar opacity mapping in 3D CT. In 2D CT, it is challenging to identify the sealant resin in the narrow leakage paths of the alloy sample due to its low grey contrast, and a machine learning approach with the Trainable Weka Segmentation (TWS) plug-in was applied to segment the CT images more precisely than by the traditional intensity-based image processing technique. TWS efficiently segmented the Al alloy, air pores, and diffused sealant resin in the samples, providing an in-depth analysis of the impregnation efficacy. Dual-energy XCT (DXCT) with photon-counting sensors was utilized as a quantitative method based on the effective atomic number to identify the impregnation material in the alloys as the commercially used Super Sealant P601 polymer resin.