Despite continuous technological improvements in metal additive manufacturing (AM) systems, process stability is still affected by several possible sources of defects especially in the presence of challenging materials. Thus, both the research community and the major AM system developers have focused an increasing attention on in situ sensing and monitoring tools in the last years. However, there is still a lack of statistical methods to automatically detect the onset of a defect and signal an alarm during the part's layer-wise production. This study contributes to this framework with two levels of novelty. First, it presents an in situ monitoring method that integrates the acquisition of infrared images with a data mining approach for feature extraction and a statistical process monitoring technique to design a data-driven and automated alarm rule. Second, the method is aimed at monitoring powder bed fusion processes for difficult-to-process materials like zinc and its alloys, which impose several challenges to the process stability and quality because of their low melting and boiling points. To this aim, the proposed approach analyzes the byproducts generated by the interaction between the energy source and the material. In particular, it detects unstable behaviors by analyzing the salient properties of the process plume to detect unstable melting conditions. This case study entails an SLM process on zinc powder, where different sets of process parameters were tested leading either to in-control or out-of-control quality conditions. A comparison analysis highlights the effectiveness of plume-based stability monitoring.
|Titolo:||In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume|
|Data di pubblicazione:||2018|
|Appare nelle tipologie:||01.1 Articolo in Rivista|
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