Researchers have developed a new infrastructure for studying fatigue crack growth in aerospace materials, enhancing traditional methods with digital tools and robotics. Their approach includes a Python library “CrackPy,” which supports multi-scale digital image correlation and automated data analysis. This integration significantly improves the information-to-cost ratio by providing detailed insights into material behavior, offering a robust alternative to conventional methods and paving the way for advanced, data-driven experimental mechanics.
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