In-Situ Defect Detection of Metal Additive Manufacturing: An Integrated Framework

Metal Additive Manufacturing (AM) is a pillar of the Industry 4.0, with many attractive advantages compared to traditional subtractive fabrication technologies. However, there are many quality issues that can be an obstacle for mass production. The in-situ camera-based monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build, can be an effective solution to this problem. In this context, the use of Computer Vision and Machine Learning algorithms have a very important role. Nonetheless, they are up to this date limited by the scarcity of data for the training, as well as by the difficulty of accessing and integrating the AM process data throughout the fabrication. To tackle this problem, this article proposes a system for in-situ monitoring that analyses images from an off-axis camera mounted on top of the machine to detect the arising defects in real-time, with automated generation of synthetic images based on Generative Adversarial Network (GAN) for dataset augmentation purposes. The computing functionalities are embedded into a holistic distributed AM platform allowing the collection, integration and storage of data at all stages of the AM pipeline.

Automated summary

Metal Additive Manufacturing (AM) is an important aspect of Industry 4.0, offering several advantages over traditional subtractive fabrication techniques. However, quality issues can hinder mass production. This article proposes a solution that utilizes computer vision and machine learning algorithms for real-time defect monitoring, with the generation of synthetic images using Generative Adversarial Network (GAN) for data augmentation.