A Software-Aided Approach to Increase Accuracy and Efficiency of Fractographic Image Analysis

Fatigue, or damage due to cyclic loading, has overwhelming importance in the assessment of the safety and integrity of structures. Damage Tolerance Analysis (DTA) relies on empirically obtained fractographic crack growth data to validate the analytical methods used in the analysis. These data are acquired through high magnification imaging of a crack face using a scanning electron microscope (SEM) and subsequent measurement of the spacing of individual fatigue striations. The current measurement method is multistep, highly labor intensive, and prone to errors in quantitative measurement. SwRI developed a Python software tool to partially automate the fatigue striation measurement and counting procedure, increasing process accuracy and significantly decreasing the time required for image analysis and feature measurements. The tool was successfully validated on a crack face generated by a known fatigue loading spectrum history. Striation generated crack growth data was shown to be within 20% of the test data, sufficient for implementation in inspection cycle management applications. Processing of the images using the tool was shown to have an efficiency increase of nearly 20X over the traditional workflow method. This increased efficiency makes the tool-based analysis method cost-effective for use in supporting enhanced DTAs.