In-situ layer-wise certification for direct laser deposition processes based on thermal image series analysis

Abstract

In direct laser deposition (DLD) processes, process uncertainty leads to defects in the final product, which can significantly compromise product quality, mechanical properties, and reliability of the additively manufactured (AM) parts. Therefore, quality control and certification are of critical importance in the broader adoption of DLD processes. In-situ thermal history contains critical information of process quality and defect occurrences. This paper proposes a new layer-wise anomaly detection method for in-situ DLD process certification by leveraging thermal image series analysis. Image registration is leveraged to characterize the dynamics in the layer-wise thermal history, and Gaussian process (GP) models are used to characterize the variation component which is left unexplained by the image registration operation. Multiple new layer-wise features are extracted from the registration modeling and the GP models. Both a thin wall specimen and a cylindrical shaped specimen are used in the case study to demonstrate the effectiveness of the proposed method. When comparing with the benchmark method, the proposed method shows comparable results for the thin wall specimen, and it significantly outperforms the benchmark method for the cylindrical shaped specimen. In addition, the average computational time of the proposed method is significantly shorter than the average layer-wise build time, enabling the proposed method to facilitate in-situ anomaly detection and process control.