PACS: Projection-driven with Adaptive CADs X-ray Scatter compensation for additive manufacturing inspection

Abstract

Additive Manufacturing (AM) has revolutionised the production of custom-shaped samples through direct manufacturing from digital design models. As the internal structural integrity of these printed samples is of critical importance in diverse applications, X-ray radiography and X-ray Computer Tomography (X-CT) have emerged as widely used non-destructive imaging methods for quality control of AM samples. Unfortunately, beam hardening and X-ray scatter often degrade the quality of X-CT images, posing a significant challenge for X-ray based inspection. In addressing X-ray scatter, most of the methodologies assume fixed scanning geometries or stationary/known object characteristics, limiting their practicality in dynamic industrial scenarios where these may change over time. Simulation-based methods have been proposed that estimate and suppress scatter by accurately simulating the forward projection process. Yet, these methods assume the availability of X-CT reconstruction for simulation, thereby requiring a large number of projections (and hence scan time) for faithful X-CT reconstruction. In this work, we propose a simulation-based scatter compensation method (PACS) that eliminates the need for a prior X-CT scan. By employing few projections and nominal surface meshes of the scanned objects, the actual pose of the objects and their superficial deviation (e.g., due to printing) are estimated and used during Xray simulations. Furthermore, as the pipeline is inherently coupled with a mesh projector, analysis of projective residuals facilitates the inspection for deformities or defects within the scanned object. To demonstrate the versatility of PACS for mitigating scatter, experiments across various inspection scenarios are conducted, and the outcomes are compared with those of a well-established scatter compensation technique. The results consistently show a higher Signal-to-Noise Ratio and Contrast-to-Noise Ratio of pore-defects, as well as lower residual errors in all examined cases.