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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 machine after previous builds or inadequate quality control during production and storage of the powder. 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 as the part is being built using in-process sensors. The central hypothesis is that transforming the process signals in the spectral graph domain leads to early and more accurate detection of material cross-contamination in L-PBF compared to the traditional delay-embedded Bon-Jenkins stochastic time series analysis techniques, such as autoregressive (AR) and autoregressive moving average (ARMA) modeling. To test this hypothesis, Inconel alloy 625 (UNS alloy 06625) test parts were made at Edison Welding Institute (EWI) on a custom-built L-PBF apparatus integrated with multiple sensors, including a silicon photodetector (with 300 nm to 1100 nm optical wavelength). During the process, two types of foreign contaminant materials, namely, tungsten and aluminum particulates, under varying degrees of severity were introduced. To detect cross-contamination in the part, the photodetector sensor signatures were monitored hatch-by-hatch in the form of spectral graph transform coefficients. These spectral graph coefficients are subsequently tracked on a Hotelling T2 statistical control chart. Instances of Type II statistical error, i.e., probability of failing to detect the onset of material cross-contamination, were verified against X-ray computed tomography (XCT) scans of the part to be within 5% in the case of aluminum contaminant particles. In contrast, traditional stochastic time series modeling approaches, e.g., ARMA, had corresponding Type II error exceeding 15%. Furthermore, the computation time for the spectral graph approach was found to be less than one millisecond, compared to nearly 100 ms for the traditional time series models tested.

This paper discusses the development, processing steps, and evaluation of a smart build-plate or baseplate tool for metal additive manufacturing technologies. This tool uses an embedded high-definition fiber optic sensing fiber to measure strain states from temperature and residual stress within the build-plate for monitoring purposes. Monitoring entails quality tracking for consistency along with identifying defect formation and growth, i.e., delamination or crack events near the build-plate surface. An aluminum alloy 6061 build-plate was manufactured using ultrasonic additive manufacturing due to the process’ low formation temperature and capability of embedding fiber optic sensing fiber without damage. Laser-powder bed fusion (L-PBF) was then used to print problematic geometries onto the build-plate using AlSi10Mg for evaluation purposes. The tool identified heat generation, delamination onset, and delamination growth of the printed L-PBF parts.

No abstract

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%.