The goal of this work is to detect the onset of material cross-contamination in laser powder bed fusion (L-PBF) additive manufacturing (AM) process using data from in-situ sensors. Material cross-contamination refers to trace foreign materials that may be introduced in the powder feedstock used in the process due to reasons, such as poor cleaning of the AM machine after previous builds, or inadequate quality control during production and storage of the feedstock powder material. Material cross-contamination may lead to deleterious changes in the microstructure of the AM part and consequently affect its functional properties. Accordingly, the objective of this work is to develop and apply a spectral graph theoretic approach to detect the occurrence of material cross-contamination in real-time during the build using in-process sensor signatures, such as those acquired from a photodetector. To realize this objective Inconel alloy 625 test parts were made on a custom-built L-PBF apparatus integrated with multiple sensors, including a photodetector (300 nm to 1100 nm). During the process the powder bed was contaminated with two types of foreign materials, namely, tungsten and aluminum powders under varying degrees of severity. Offline X-ray Computed Tomography (XCT) and metallurgical analyses indicated that contaminant particles may cascade to over eight subsequent layers of the build, and enter up to three previously deposited layers. This research takes the first-step towards detecting cross-contamination in AM by tracking the process signatures from the photodetector sensor hatch-by-hatch invoking spectral graph transform coefficients. These coefficients are subsequently traced on a Hoteling T2 statistical control chart. Using this approach, instances of Type II statistical error in detecting the onset of material cross-contamination was 5% in the case of aluminum, in contrast, traditional stochastic time series modeling approaches, e.g., ARMA had corresponding error exceeding 15%.

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