A Knowledge Constrained Deep Spatio-Temporal Clustering Approach for Melt Pool Anomaly Detection in Laser Powder Bed Fusion

Published

March 25, 2024

Author(s)

Zhuo Yang, Yan Lu, Erfan Ziad, Feng Ju

Abstract

The rapid expansion of the manufacturing sector has brought laser-based metal additive manufacturing, like laser powder bed fusion, to the forefront of innovation. Yet, its widespread acceptance hinges on overcoming numerous obstacles, including uncertainties regarding part quality when employing standardized materials in additive manufacturing procedures. Clustering techniques are essential in uncovering patterns within data sets, particularly in the field of additive manufacturing, where understanding the behavior of meltpool images is crucial for process optimization. Traditional hierarchical clustering methods often lack the ability to incorporate domain-specific knowledge, limiting their effectiveness in this domain. In this study, we propose a novel approach that integrates prior knowledge-constrained hierarchical clustering with encoded meltpool image sequences. By incorporating domainspecific constraints, our approach aims to enhance clustering accuracy and provide more interpretable cluster assignments. Our approach improves clustering performance and provides insights into melt pool image sequences, enabling us to evaluate printed parts' quality.

Proceedings Title

A Knowledge Constrained Deep Spatio-Temporal Clustering Approach for Melt Pool Anomaly Detection in Laser Powder Bed Fusion

Conference Dates

August 28-September 1, 2024

Conference Location

Bari, IT

Conference Title

IEEE CASE 2024

Pub Type

Conferences

Citation

Yang, Z. , Lu, Y. , Ziad, E. and Ju, F. (2024), A Knowledge Constrained Deep Spatio-Temporal Clustering Approach for Melt Pool Anomaly Detection in Laser Powder Bed Fusion, A Knowledge Constrained Deep Spatio-Temporal Clustering Approach for Melt Pool Anomaly Detection in Laser Powder Bed Fusion, Bari, IT (Accessed July 17, 2024)