Evaluation of Key Spatiotemporal Learners for Print Track Anomaly Classification Using Melt Pool Image Streams

TL;DR

This paper evaluates advanced spatiotemporal deep learning models for classifying melt pool image streams in metal additive manufacturing, highlighting the robustness of the pre-trained SlowFast network under data perturbations.

Contribution

It introduces and compares multiple deep spatiotemporal models for melt pool classification, emphasizing the superior generalization of the SlowFast network in real-world scenarios.

Findings

SlowFast network shows strong robustness to data perturbations.

Two-stream and recurrent models effectively capture spatiotemporal features.

Limited exploration of models' generalization in additive manufacturing context.

Abstract

Recent applications of machine learning in metal additive manufacturing (MAM) have demonstrated significant potential in addressing critical barriers to the widespread adoption of MAM technology. Recent research in this field emphasizes the importance of utilizing melt pool signatures for real-time defect prediction. While high-quality melt pool image data holds the promise of enabling precise predictions, there has been limited exploration into the utilization of cutting-edge spatiotemporal models that can harness the inherent transient and sequential characteristics of the additive manufacturing process. This research introduces and puts into practice some of the leading deep spatiotemporal learning models that can be adapted for the classification of melt pool image streams originating from various materials, systems, and applications. Specifically, it investigates two-stream networks comprising spatial and temporal streams, a recurrent spatial network, and a factorized 3D convolutional neural network. The capacity of these models to generalize when exposed to perturbations in melt pool image data is examined using data perturbation techniques grounded in real-world process scenarios. The implemented architectures demonstrate the ability to capture the spatiotemporal features of melt pool image sequences. However, among these models, only the Kinetics400 pre-trained SlowFast network, categorized as a two-stream network, exhibits robust generalization capabilities in the presence of data perturbations.