Abstract
Quality assurance is a major concern driving machine manufacturers to introduce in-situ monitoring modules in commercial Laser-Powder Bed Fusion (L-PBF) machines. The post-treatment of the humongous amount of in-situ data and the extraction of key process indicators (KPIs) are a topic of research. This study proposes a methodology to extract critical characteristics from the melt pool monitoring (MPM) and layer control system (LCS) data using statistical, machine learning, and computer vision techniques. The variabilities in MPM data are monitored at local (melt pool level) and global scales (layer level). A qualitative comparison between the MPM data and Computed Tomography scan is made for Ti6Al4V alloy. Alongside, a case study to investigate the link between LCS and MPM data is presented. The proposed methodology can be used to assess the parts qualitatively.
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