Laser metal deposition (LMD) is recognized as a critical technique in Additive Manufacturing (AM) that allows the production and repair of components in a high-quality, efficient, and cost-effective manner. However, defects may still arise in the deposited components. While conventional architectures like Convolutional Neural Networks (CNNs) have shown satisfactory results in detecting these defects using images captured during the process, transformer-based models remain relatively underexplored in this context.

This study focused on designing a transformer-based architecture that could achieve high accuracy in identifying anomalies through melt pool images obtained during the wirefed LMD process. Upon its development, it was used to crossreference the predictions of an existing powerful CNN approach to ensure the reliability of its outcomes.

Initially, the algorithm was trained using a custom Vision Transformer-decoder architecture with no labels involved, resulting in an accuracy of 92.66%. By utilizing the captured information from the classification token, its ability to identify anomalies was significantly improved, achieving 99.78% in a 900-image dataset.

However, when evaluated on 6,497 unseen frames from the process with ground truth predictions generated by the CNN model, ViT’s accuracy decreased to 97.83%, a result attributed to the specific training method and the variability in the test set. Despite this reduction, the results were considered satisfactory, given the relatively new application of transformers on images, which has not been extensively explored in the field of anomaly detection.

Overall, this research offers a comprehensive explanation of the proposed model architecture and outlines the necessary modifications required to achieve near-perfect performance on a transformer-based architecture, paving the way for future enhancements in anomaly detection.