Supplementary Information for "Classification of microstructural defects in selective laser melted inconel 713C alloy using convolutional neural networks".

This work explores the performance of Convolutional Neural Networks in classifying the variety of microstructural defects found in additively manufactured Nickel alloys. This repository contains supplementary information regarding the publication and the work associated with it. Included in it is: code used to extract binary images of defects from micrographs, a matlab live script containing information used to train the models, and the datasets used in model training.

This work was mainly conducted in MATLAB R2023b. Python and some of it's libraries have been used for minor data analysis.

To cite the work: Edmunds EL, Thomas M. Classification of microstructural defects in selective laser melted inconel 713C alloy using convolutional neural networks. Materials Science and Technology. 2025;0(0). doi:10.1177/02670836241308470

Original micrographs sourced from: Liu C. Selective laser melting of nickel superalloys for aerospace applications: defect analysis and material property optimisation. PhD Thesis, The University of Sheffield, UK, 2021.