In-process 3D Deviation Mapping and Defect Monitoring (3D-DM2) in High Production-rate Robotic Additive Manufacturing

2025-11-06

AI Summary

To address uncontrolled geometric inaccuracy in high-deposition-rate robotic additive manufacturing (HDR-RAM) caused by open-loop operation, this paper proposes the 3D-DM² framework—the first real-time 3D deviation mapping and defect monitoring system tailored for cold-spray processes. The method integrates robot-path-synchronized online 3D scanning, lightweight point-cloud registration, incremental surface reconstruction, and layer-wise deviation segmentation to enable dynamic identification, continuous tracking, and early-stage defect detection during deposition. Experimental results demonstrate stable sub-millimeter deviation detection under high deposition rates, enabling closed-loop geometric compensation. This significantly reduces post-processing requirements and enhances dimensional consistency and process stability for complex components.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Additive manufacturing (AM) is an emerging digital manufacturing technology to produce complex and freeform objects through a layer-wise deposition. High deposition rate robotic AM (HDRRAM) processes, such as cold spray additive manufacturing (CSAM), offer significantly increased build speeds by delivering large volumes of material per unit time. However, maintaining shape accuracy remains a critical challenge, particularly due to process instabilities in current open-loop systems. Detecting these deviations as they occur is essential to prevent error propagation, ensure part quality, and minimize post-processing requirements. This study presents a real-time monitoring system to acquire and reconstruct the growing part and directly compares it with a near-net reference model to detect the shape deviation during the manufacturing process. The early identification of shape inconsistencies, followed by segmenting and tracking each deviation region, paves the way for timely intervention and compensation to achieve consistent part quality.

Problem

• Real-time monitoring detects shape deviations in robotic additive manufacturing
• Addressing process instabilities in open-loop high deposition rate systems
• Early identification of shape inconsistencies enables timely intervention

Innovation

• Real-time monitoring of growing part geometry
• Direct comparison with near-net reference model
• Segmentation and tracking of deviation regions