Surface monitoring is an essential part of quality assurance for additive manufacturing (AM). Surface defects need to be identified early in the AM process to avoid further deterioration of the part quality. In this paper, a rapid surface defect identification method for directed energy deposition (DED) is proposed. The main contribution of this work is the development of an in-situ point cloud processing with machine learning methods that enable automatic surface monitoring without sensor intermittence. An in-house software platform with a multi-nodal architecture is developed. In-situ point cloud processing steps, including filtering, segmentation, surface-to-point distance calculation, point clustering, and machine learning feature extraction, are performed by multiple subprocesses running simultaneously. The combined unsupervised and supervised machine learning techniques are applied to detect and classify surface defects. The proposed method is experimentally validated, and a surface defect identification accuracy of 93.15% is achieved.

Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser-directed energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defect correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production.