This work introduces a methodology for evaluting crystallite sizes from two-dimensional X-ray diffraction (2DXRD) patterns, addressing key challenges such as abnormal grain size distributions and phase transitions. These challenges are particularly pertinent in thin film materials, where the transition from small to large grains significantly influences material properties.

Our method leverages automated image recognition techniques to detect and localize diffraction spots within 2D-XRD images. By integrating advanced data modeling and noise reduction strategies, we enhance the precision of crystallite size estimation. The process involves a detailed analysis of diffraction spot positions and intensities—critical factors for accurate size determination.

To improve detection accuracy, we employ a suite of image processing techniques, including thresholding, peak detection, and edge identification. These methods ensure reliable spot identification, even in complex samples exhibiting a mix of grain sizes. We also implement rigorous background subtraction to refine intensity measurements, which is essential for precise crystallite size assessment.

In scenarios where diffraction peaks are closely positioned, we introduce an approximation method to estimate the smallest resolvable crystallite size. This approach addresses a common limitation in 2D-XRD analysis by enabling the accurate calculation of the maximum number of grains that can be analyzed using our technique.

However, the method is sensitive to grain orientation, which can affect measurement accuracy. Despite this, our approach provides a robust framework for analyzing grain growth in thin films, offering valuable insights into material behavior during grain evolution.

This contribution aims to enhance process-oriented X-ray analysis by providing an automated, precise approach to crystallite size determination, with broad applications in both research and industrial settings.