High-speed Interpretable Representation Learning of Melt Pool Signatures in Metal Additive Manufacturing

Abstract

Additive Manufacturing (AM), i.e. 3D printing, of metal parts is growing in popularity, but part quality can still be a limiting factor to that growth. Camera-based monitoring systems improve quality by detecting defects on-the-fly, but it often relies on incomplete handcrafted video features, or hard to interpret features derived by data-driven techniques. In this work, we propose a method based on variational autoencoders (VAEs) that produces highly informative and interpretable features for in-situ AM monitoring. Unlike handcrafted features, our technique is data-driven so that it captures all details. Unlike other data-driven methods, our technique produces features that are highly interpretable and correlate to the involved physics in the printing process. To test our technique, an object is printed with deliberate non-nominal layers and recorded at high speed with a monochrome optical camera. The video frames are then used to train a VAE as feature extractor. The VAE-computed features are then input into a classification algorithm to detect print deviations. It is shown that our proposed features outperform state-of-the-art deep learning methods, and handcrafted features, with up to 2.16% improvement, all while preserving feature interpretability, the completeness of data-driven feature extraction, and high computation speed (> 5 kHz).