Optimizing High-Throughput SEM for Large-area Defect Characterization in AM Steel

Scanning electron microscopy (SEM) is an important method for detailed microstructural analysis in materials science, providing information about structural details that other imaging techniques cannot achieve. However, to enable high-resolution feature characterization and large-area imaging in a time-efficient manner, which is essential for the statistical relevance of such studies, high-throughput SEM becomes indispensable. This requires optimization of SEM imaging parameters as well as feature detection in the SEM images acquired under such conditions, for example, dwell time and pixel size must be carefully balanced during data acquisition to ensure both efficiency and accuracy.

This study introduces a high-throughput SEM approach for automated defect detection in additively manufactured (AM) steel, used as our model metal material system. The method leverages local contrast variations in secondary electron (SE) imaging to improve feature identification. We conduct a systematic evaluation of the trade-offs between data acquisition speed and accuracy in structural defect recognition, allowing us to determine error rates and assess the impact of imaging parameters on defect identification. By quantitatively analyzing how variations in acquisition settings influence precision and reliability, we ensure that the optimization process not only enhances efficiency but also maintains robust defect characterization.

By effectively balancing SEM image acquisition speed and accuracy, this scalable approach improves the efficiency of high-throughput SEM imaging and enables reliable analysis of the type and size of structural defects in our AM steel samples under various imaging conditions. The insights gained from this evaluation provide a guideline for optimizing SEM imaging parameters, ensuring consistent and robust microstructural characterization while maximizing acquisition efficiency.