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The ability of additive manufacturing (AM) processes to produce components with virtually any geometry presents a unique challenge in terms of quantifying the dimensional quality of the part. In this paper, a novel spectral graph theory (SGT) approach is proposed for resolving the following critical quality assurance concern in AM: how to quantify the relative deviation in dimensional integrity of complex AM components. Here, the SGT approach is demonstrated for classifying the dimensional integrity of standardized test components. The SGT-based topological invariant Fiedler number (λ2) was calculated from 3D point cloud coordinate measurements and used to quantify the dimensional integrity of test components. The Fiedler number was found to differ significantly for parts originating from different AM processes (statistical significance p-val. < 1%). By comparison, prevalent dimensional integrity assessment techniques, such as traditional statistical quantifiers (such as mean and standard deviation) and examination of specific facets/landmarks failed to capture part-to-part variations, and thus proved incapable of ranking the quality of test AM components in a consistent manner. In contrast, the SGT approach was able to consistently rank the quality of the AM components with a high degree of statistical confidence independent of sampling technique used. Consequently, from a practical standpoint, the SGT approach can be a powerful tool for assessing the dimensional integrity of AM components, and thus encourage wider adoption of AM capabilities.

The aim of this paper is to demonstrate a pathway for in situ real-time monitoring and closed-loop control of aerosol jet printing (AJP) process. To achieve this aim, we instrumented an Optomec AJ-300 aerosol jet printer with multiple temporal and image-based sensors. Experiments were conducted by varying the sheath gas flow rate (ShGFR) and, subsequently, the line morphology was acquired online using a CCD camera mounted coaxial to the nozzle (perpendicular to the platen). To assess the line morphology, we devised a novel digital image processing method that quantifies aspects of line morphology, such as line density, overspray, continuity, edge smoothness, etc. As a result, an optimal process window was established. Next, the underlying aerodynamic phenomena that influence the line morphology are explained based on a two dimensional computational fluid dynamics (2D-CFD) model. Thus, the image processing approach proposed in this work can be used to detect incipient process drifts, while the CFD model will be valuable to suggest the appropriate corrective action to bring the process back in control. We further validate that there is a good agreement between the online and offline results with respect to the quantified morphology of the lines.

The objective of this work is to identify failure modes and detect the onset of process anomalies in Additive Manufacturing (AM) processes, specifically focusing on Fused Filament Fabrication (FFF). We accomplish this objective using advanced Bayesian non-parametric analysis of in situ heterogeneous sensor data. The proposed method can ultimately lead to intelligent decision making and closed loop control in AM processes. Experiments are conducted on a desktop FFF machine (MakerBot Replicator 2X) instrumented with a heterogeneous sensor array including thermocouples, accelerometers, an infrared temperature sensor, and a real-time miniature video borescope. FFF process failures are detected online using the non-parametric Bayesian Dirichlet Process (DP) mixture model and evidence theory based on the experimentally acquired sensor data. This sensor data-driven defect detection approach facilitates real-time identification and correction of FFF process drifts with an accuracy and precision approaching 85% (average F-score). In comparison, the F-score from existing approaches, such as Probabilistic Neural Networks, Naive Bayesian Clustering, Support Vector Machines, and Quadratic Discriminant Analysis was in the range of 55% to 75%.Copyright © 2015 by ASME