Machine learning applied to identify corrosive environmental conditions

The reliability of turbine engines depends significantly on the environment experienced during flight. Air humidity, corrosive contaminant substances, and high operating temperatures are among the attributes that affect engine lifespans. The specifics of the environment that affect materials are not always known, and damage is often evaluated by time-consuming manual inspection. This study innovates by demonstrating that machine learning approaches can identify the environmental conditions that degrade jet engine metallic materials. We used the state-of-the-art pre-trained neural network models to assess images of damaged nickel-based superalloy samples to identify the environment temperature, the exposure time, and the deposited amounts of salt contaminants. These parameters are predicted by training the model with a database of approximately 3,600 sample images tested in laboratory conditions. A novel tree classification process results in excellent predictive power for classifying the type of environment experienced by nickel-based superalloys.