In situ monitoring of thin-wall build quality in laser powder bed fusion using deep learning

The goal of this work is to mitigate flaws in metal parts produced from the laser powder bed fusion (LPBF) additive manufacturing (AM) process. As a step toward this goal, the objective of this work is to predict the build quality of a part as it is being printed via deep learning of in situ layer-wise images acquired using an optical camera instrumented in the LPBF machine. To realize this objective, we designed a set of thin-wall features (fins) from titanium alloy (Ti-6Al-4V) material with a varying length-to-thickness ratio. These thin-wall test parts were printed under three different build orientations, and in situ images of their top surface were acquired during the process. The parts were examined offline using X-ray computed tomography (XCT), and their build quality was quantified in terms of statistical features, such as the thickness and consistency of its edges. Subsequently, a deep learning convolutional neural network (CNN) was trained to predict the XCT-derived statistical quality features using the layer-wise optical images of the thin-wall part as inputs. The statistical correlation between CNN-based predictions and XCT-observed quality measurements exceeds 85 %. This work has two outcomes consequential to the sustainability of AM: (1) it provides practitioners with a guideline for building thin-wall features with minimal defects, and (2) the high correlation between the offline XCT measurements and in situ sensor-based quality metrics substantiates the potential for applying deep learning approaches for the real-time prediction of build flaws in LPBF.