Abstract
Thermal and mechanical processes alter the microstructure of materials, which determines their mechanical properties. This makes reliable microstructural analysis important to the design and manufacture of components. However, the analysis of complex microstructures, such as Ti6Al4V, is difficult and typically requires expert materials scientists to manually identify and measure microstructural features. This process is often slow, labour intensive and suffers from poor repeatability. This paper overcomes these challenges by proposing a new set of automated techniques for 2D microstructural analysis. Digital image processing algorithms are developed to isolate individual microstructural features, such as grains and alpha lath colonies. A segmentation of the image is produced, where regions represent grains and colonies, from which morphological features such as; grain size, volume fraction of globular alpha grains and alpha colony size can be measured. The proposed measurement techniques are shown to obtain similar results to existing manual methods while drastically improving speed and repeatability. The benefits of the proposed approach when measuring complex microstructures are demonstrated by comparing it with existing analysis software. Using a few parameter changes, the proposed techniques are effective on a variety of microstructure types and both SEM and optical microscopy images.
Highlights
Measuring the size of alpha lath colonies remains a manual task in these procedures. Within both industry and academia, manual procedures are currently relied upon to measure the complex microstructures used in our study due to the lack of reliable techniques for identifying and measuring alpha grains
We focus on the titanium alloy Ti6Al4V
We proposed pre- and postprocessing techniques in order to improve the segmentation accuracy achieved by the Watershed Algorithm, when applied to challenging microstructural images
Summary
The software can compute the orientation of the image which can help identify lath colonies in microstructural images, no fully automated method for computing their size is provided Some features, such as the volume fraction of alpha phase and width of alpha laths, can be measured relatively successfully by existing techniques, as a result these are not investigated further in this study. Only Sosa et al [17] present automated methods for the analysis of alpha grains with all others still requiring manual input to measure this feature. The method proposed in [17] relies on boundaries presenting with a dark or light line at their boundary, which is not always possible when imaging many microstructures This affects the reliability of measurements of alpha grain size and volume fraction of globular alpha. We apply our techniques to measure primary alpha grain size, the volume fraction of globular primary alpha and the size of alpha lath colonies
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