In the recent years, several studies and industrial developments have been devoted to the improvement of process repeatability, stability and robustness to enhance the industrial breakthrough of Additive Manufacturing (AM) technologies. Indeed, highly regulated sectors like aerospace and healthcare have been pulling the industrial innovation in metal AM, and this makes defect avoidance and qualification issues of fundamental importance. This imposes an urgent need for novel in-line and in-situ qualification and control tools able to guarantee a stable process and defect-free products. On the one hand, the layerwise paradigm of AM processes enables the capability of acquiring a large amount of data during the process to measure quality characteristics of the part and measure process signatures that are proxies of the process stability over time. On the other hand, data mining and statistical methods are needed to make sense of big data streams gathered in-line and in-situ, to design automated and robust defect detection tools. This paper reviews the opportunities and challenges related to in-situ sensing and monitoring solutions for zero-defect and first-time-right AM processes, with a special focus on metal Powder Bed Fusion (PBF) processes.
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