This project evaluates the performance of Lab_HEDM.py, a preprocessing script designed by Seunghee Oh of the University of Michigan, to remove unwanted secondary peaks from X-ray Diffraction databy generating synthetic datasets and developing a new testing algorithm (Algorithm.py) that employs OpenCV morphological transformations and HEXRD’s find_peaks_2d() for enhanced peak segmentation. Preliminary tests indicate that Lab_HEDM.py has 96.38% success rate in secondary peak removal, though misidentified peaks in the evaluation algorithm underscore the need for further refinements. Going forward, efforts focus on refining peak detection, improving synthetic data to better match real diffraction complexities, and integrating more robust background replacement methods into Lab_HEDM.py for increased accuracy and reliability.