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

TL;DR

This paper introduces a real-time 3D deviation mapping system for high-speed robotic additive manufacturing, enabling early defect detection and process correction to improve part accuracy.

Contribution

It presents a novel in-process 3D deviation monitoring method specifically designed for high-rate robotic additive manufacturing processes.

Findings

Effective real-time shape deviation detection during manufacturing

Segmentation and tracking of deviation regions for targeted correction

Enhanced process stability and part quality control

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.