Multi-modal Sensing for Additive Manufacturing

Additive manufacturing, particularly directed energy deposition (DED), is highly sensitive to process-induced instabilities that lead to geometric deviations, lack of fusion, and poor surface finish. These defects often originate and evolve layer by layer, making timely and quantitative in-situ sensing essential for ensuring part quality and enabling closed-loop process control. This project investigates multi-modal sensing strategies for additive manufacturing, with a focus on linking real-time process signatures to the evolving part geometry.

We develop a build-height–synchronized fringe projection profilometry system for in-situ, layer-wise 3D surface reconstruction during laser-based DED. The system achieves micrometer-scale accuracy and enables full-field measurement of surface morphology after each deposited layer without interrupting the manufacturing process. Based on the reconstructed surface point clouds, geometry-driven metrics including local point density and normal-change rate are introduced to automatically and annotation-free identify common deposition anomalies such as lack of fusion, under-deposition, and surface collapse. This approach shifts in-situ monitoring from indirect intensity- or thermal-based proxies to direct geometric evidence (Hu et al., 2026; Hu et al., 2024).

With Prof. Florian Willomitzer’s Computational 3D Imaging and Measurement Lab at the University of Arizona, we also developed a fast multi-view polarized fringe projection system for 360° inspection inside the DED machine. Camera-projector pairs placed around the deposition area measure the surface in a single shot from several viewpoints, and cross-polarized filtering suppresses specular reflections from shiny alloys (Taylor et al., 2026).

2026

Layer-wise anomaly detection in directed energy deposition using high-fidelity fringe projection profilometry

Guanzhong Hu , Wenpan Li , Rujing Zha , and Ping Guo

Journal of Manufacturing Processes, 2026

Abstract PDF

Directed energy deposition (DED), a metal additive manufacturing process, is highly susceptible to process-induced defects such as geometric deviations, lack of fusion, and poor surface finish. This work presents a build-height-synchronized fringe projection system for in-situ, layer-wise surface reconstruction of laser-DED components, achieving a reconstruction accuracy of ±46µm. From the reconstructed 3D morphology, two complementary geometry-based point-cloud metrics are introduced: local point density, which highlights poor surface finish, and normal-change rate, which identifies lack-of-fusion features. These methods enable automated, annotation-free identification of common deposition anomalies directly from reconstructed surfaces, without the need for manual labeling. By directly linking geometric deviation to defect formation, the approach enables precise anomaly localization and advances the feasibility of closed-loop process control. This work establishes fringe projection as a practical tool for micrometer-scale monitoring in DED, bridging the gap between process signatures and part geometry for certifiable additive manufacturing.

Fast 360° 3D Metrology for Directed Energy Deposition

James Taylor , Jiazhang Wang , Guanzhong Hu , Zihan Chen , Rowan Rolark , Jian Cao , Ping Guo , and Florian Willomitzer

Journal of Manufacturing Processes, 2026

Abstract PDF

Directed Energy Deposition (DED) is a metal additive manufacturing process capable of building and repairing large, complex metal parts from a wide range of alloys. Its flexibility makes it attractive for multiple industrial applications, e.g., in aerospace, automotive and biomedical fields. However, errors or defects introduced at any stage of the printing process can, if undetected, significantly impact the final result, rendering the printed part unusable. Potential in-situ methods for correcting printing defects require fast and high-resolution on-the-fly 3D inspection inside the machine, but existing 3D imaging techniques often lack full surface coverage, require bulky setups, or operate too slowly for layer-wise feedback. In this paper, we present a fast, multi-view polarized fringe projection profilometry (FPP) system designed for real-time 3D inspection during DED printing. Multiple camera-projector pairs are arranged around the deposition area to measure the 3D surface in a single shot from different viewpoints, while cross-polarized image filtering suppresses specular reflections caused by varying surface reflectance across different alloys. The final 360° reconstruction is obtained via joint registration of the captured multi-view measurements. Our prototype has been deployed inside a DED system, and our first experiments demonstrate a depth precision of δz < 56 µm and a depth accuracy of 64 µm, both evaluated on different partially reflective and "shiny" metal surfaces, enabling high-quality, layer-wise monitoring for closed-loop DED control.

2024

Digital fringe projection for interlayer print defect characterization in directed energy deposition

Guanzhong Hu , Rujing Zha , Yaoke Wang , Jian Cao , and Ping Guo

In 2024 International Symposium on Flexible Automation , Jul 2024

Abstract PDF

Directed Energy Deposition (DED) is one of the main additive manufacturing (AM) families, enabling the fabrication of multi-material parts with high material addition rates. However, the incremental nature of DED fabrication makes it prone to local defect formation due to process condition fluctuations. Known for its rapid and precise 3D surface measurement capabilities, digital fringe projection (DFP) was previously demonstrated in process monitoring for powder bed AM. This study brings DFP to the DED process through development of a custom motor stage system and validates its effectiveness in assessing surface topography and build height measurement. Measurements were taken on both correctly deposited builds and builds with off-nominal deposition conditions, where the system was able to detect pitting as small as 0.425 mm in the lateral size and 0.154 mm in depth in the case of reduced laser energy. This work paves the way for future machine learning-enabled interlayer defect identification, classification, and healing via altering subsequent processing settings.

References

2026

2024