Case study on the application of microstructural features extracted by convolutional neural network for cold spray of aluminum alloys

The use of process-microstructure-property relationships for cold spray can significantly reduce time and cost of application development compared to legacy trial and error strategies. However, due to the heterogeneous microstructure of a cold spray deposit, with (prior) particle boundaries outlining consolidated splats (deformed particles) in the as-spray condition, the use of automated analysis methods is challenging. In this work, we demonstrate the utility of quantitative data developed from a convolutional neural network (CNN) for feature extraction of cold spray microstructures. Specifically, the power of CNN is harnessed to automatically segment the deformed particles, which is difficult to match at scale with traditional image processing techniques. Deposits produced with various processing conditions are evaluated with metallography and micro-hardness tests. Parameters related to particle morphology such as aspect ratio and compactness are also quantified and correlated to micro-hardness.