The manufacture of defect-free parts has been a key discussion topic with the widespread adoption of additive manufacturing by industry. While significant research has been performed on the detection of powder bed defects, the focus has been on the classification of the defects according to defect type. However, when looking at creating a closed loop feedback system, it is important for the machine to make autonomous decisions regarding defects. The focus of this paper will be to create a defect severity classification matrix based on industry partner experience as well as published literature that can be used to autonomously classify defects

Over time, the Internet of things (IoT) discussion has come to prominence, and in-situ monitoring systems have been geared up with IoT services and deployed over IoT architectures. The integration of IoT services within system development has enriched many monitoring application studies but the architectural models used in majority of these studies trivializes several key components of a fully functional IoT architecture. This paper proposes a general-purpose architectural model (GPAM) that can be used in the deployment of any in-situ monitoring system. The proposed architectural model is successfully implemented using a single domain use-case (water assessment) as a conceptual proof. The traditional water assessment processes are transformed into IoT processes in an attempt to reach the same result in a more efficient manner. This template can help prospective developers build and engineer robust IoT monitoring systems.