A key challenge in metal additive manufacturing is the prevalence of defects, such as discontinuities within the part (e.g., porosity). The objective of this work is to monitor porosity in Laser Powder Bed Fusion (L-PBF) additive manufacturing of nickel alloy 718 (popularly called Inconel 718) test parts using in-process optical emission spectroscopy. To realize this objective, cylinder-shaped test parts are built under different processing conditions on a commercial L-PBF machine instrumented with an in-situ multispectral photodetector sensor. Optical emission signatures are captured continuously during the build by the multispectral sensor. Following processing, the porosity-level within each layer of a test part is quantified using X-ray Computed Tomography (CT). The graph Fourier transform coefficients are derived layer-by-layer from signatures acquired from the multispectral photodetector sensor. These graph Fourier transform coefficients are subsequently invoked as input features within various machine learning models to predict the percentage porosity-level in each layer with CT data taken as ground truth. This approach is found to predict the porosity on a layer-by-layer basis with an accuracy of ∼90% (F-score) in a computation time less than 0.5 seconds. In comparison, statistical moments, such as mean, variation, etc., are less accurate (F-score ≈ 80%) and require a computation time exceeding 5 seconds.
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering