In-situ monitoring of an Additive Manufacturing (AM) process is the way to enhance the quality of the components manufactured by it. However, the metal AM processes are complex to monitor because of usage of high energy-based heat source in melting the deposition material, particularly in the arc-based AM processes where arc and sparks make it difficult to capture the deposition. This paper explores the use of a High Dynamic Range (HDR) camera to capture and monitor deposition processes for μ-Plasma Transferred Arc Additive Manufacturing (μP-TAAM) process. Additionally, it proposes the YOLO-based object detection model to assess and monitor the quality of Co-Cr-Mo-4Ti depositions. The research focuses on analysing the performance of YOLOv8l, YOLOv9t, YOLOv9s, YOLOv9m and YOLOv10n models to detect and classify good and bad depositions. It has been found that YOLIv9m gave a strong balance across all evaluation metrics such as highest Recall of 0.983, high precision of 0.98, high mAP50 of 0.994 and high mAP50-95 of 0.848. These findings underscore the model's potential for deployment in in-situ monitoring scenarios.

Feature-driven CAPP is the dominant model for computer-aided process planning today. Despite more than a decade of effort, there are no general methods for defining or identifying features, and consequently CAPP approaches in full generality could not been developed. An alternative to feature-driven CAPP is direct tool access-based models for process planning, which rely on the fundamental geometric information in the CAD model to develop the process plan. In this paper we check the accessibility of part surfaces from different tool access directions, and use this to generate the process plan for rotational parts (turning) and for milling. Features are still useful, especially for communicating with the shop floor user, the part faces are clustered into features for the purpose of process detailing. Thus features may be seen as being driven by the process plan and not the other way round.