Superimposing Synthetic Defects into Real XCT Data and Segmentation-Based Comparison for Advanced Probability of Detection Evaluation

Abstract This research proposes an approach for integrating realistic defects into computed tomography (XCT) scans by using X-ray simulations. It allows full control over different scenarios and measuring the detection algorithm efficiency in real-world situations. Using real XCT data of a pin-fin cooler made of aluminum alloy with complex internal structures, synthetic spherical and irregular defects ranging from 56 $$\upmu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> m to 300 $$\upmu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> m in diameter are superimposed to create a comprehensive dataset that mimics a wide range of realistic scenarios. This XCT dataset with superimposed defects is then utilized to apply a probability of detection analysis to detect defects of varying sizes and shapes. This analysis shows that for spherical pores, the detectability limit is up to 2.5 times higher in the superimposed case with a minimum voxel similarity of 95%, while for irregular pores, this limit is 3.3 times higher when a minimum voxel similarity of 80%. The integration of synthetic defects into real XCT images allows for a more rigorous and controlled assessment of detection algorithms, providing valuable insights into their performance under realistic conditions. Our findings demonstrate that this method can significantly improve the accuracy and reliability of measurements of defect detectability, offering a powerful tool for quality assurance in critical manufacturing processes.