Defective microelectronics pose significant risks to industries, governments, and societies. Efficient identification of defective parts is critical to prevent or mitigate potential damages. Imaging is one of the methods used for detecting defects, but both manual and automated approaches suffer from the lack of ground truth data from defective parts. This limitation hinders the ability of subject matter experts and machine learning models to effectively recognize and classify new cases of defective parts. In this proposal, we propose a data augmentation workflow that generates virtual defective/counterfeit parts, thereby overcoming the lack of data problem and enabling the creation of large datasets at low cost. Our data augmentation workflow further generates automatically labelled ground truth data for training machine-learning-based image segmentations which would enable reverse engineering of parts from multiple modalities of images. The workflow will be implemented and available via a cloud-based platform which not only enables the use of the services without the need for any special software/hardware on the user’s side, but also allows users from remote geographical locations to collaborate on image visualization and analysis through shared sessions, which is particularly useful for workforce training purposes.