Abstract
The abilities to both monitor and control additive manufacturing (AM) processes in real-time are necessary before the routine production of quality AM parts will be possible. Currently, neither ability exist! The major reason is that AM processes are different from traditional manufacturing processes in many ways and so are the sensors and the monitoring data collected from them. In traditional manufacturing, that data is mostly numeric in nature. To that numeric data, AM monitoring data add large volumes of a variety of in situ, high-speed, image data. Collecting, fusing, and analyzing all that AM data and making the necessary control decisions is not possible using traditional, rigid, hierarchical-control architectures. Therefore, researchers are proposing to use real-time, machine-learning algorithms to analyze the data and to execute the other control functions. This paper identifies those control functions and proposes a new architecture to integrate them. This paper also shows an example of using that architecture to analyze the melt-pool, shape analysis using a clustering method.
Highlights
Within the decade, Additive Manufacturing (AM) is expected to become the primary process for creating complex geometries
The case study dataset was generated at the Additive Manufacturing Metrology Testbed (AMMT)
To check the accuracy of the proposed approach, we observe the bottom left of Figure 5 for clustering and we find that the (C4) cluster color cyan shows that this cluster has only a very few number of melt pools as compared to other melt pool
Summary
Additive Manufacturing (AM) is expected to become the primary process for creating complex geometries. Will still require four new technologies that include (1) AM software for high-complexity product design and engineering, (2) AM machines for high-quality and low-cost product fabrication, (3) AM sensors for in situ monitoring of various AM processes, and (4) controllers that can analyze big data and make optimal decisions in real-time [1]. Sci. 2020, 10, 6616 the functions in those control loops It describes the major offline functions, typically executed in the cloud, that interact with those control functions.
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