Description The near-α titanium alloy Ti834 is one of the most widely used alloys for compressor disc applications in the high-pressure section of the jet engine. The exceptional fatigue and creep properties of near-α alloys come from their bimodal microstructure formed by primary alpha grains (αp) and secondary alpha colonies (αs) obtained through carefully controlled thermomechanical processing in both the β and α+β phase regions. There are, however, local regions in the microstructure where individual alpha grains have similar crystal orientation. These microtextured regions are known as macrozones and they lead to reduction in fatigue life when the material is exposed to relatively high stress for a period of time. During service, this component is subjected to high loads and rotational speeds and when the stresses are held constant for a period of time during the loading cycle, a failure mode called cold dwell fatigue takes place leading to catastrophic failure. The EBSD (Electron Backscattered Diffraction) technique provides an orientation map with information about the spatial orientation distribution of a given crystal structure, but further data analysis is needed to identify macrozones. In this project, a post-processing technique will be developed using Python for automatic detection of large regions with similar orientation to define the size and shape of the macrozones. However, you will not be starting from scratch. Some work has already been undertaken using Matlab and some positive results have been observed. Your task will be to initially convert this code to Python, then develop and produce a more robust code that can be used for external use, for companies like Rolls-Royce.