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 nonparametric analysis of in situ heterogeneous sensor data. Experiments are conducted on a desktop FFF machine instrumented with a heterogeneous sensor array including thermocouples, accelerometers, an infrared (IR) temperature sensor, and a real-time miniature video borescope. FFF process failures are detected online using the nonparametric Bayesian Dirichlet process (DP) mixture model and evidence theory (ET) 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 (PNN), naïve Bayesian clustering, support vector machines (SVM), and quadratic discriminant analysis (QDA), was in the range of 55–75%.

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The objectives of this paper in the context of aerosol jet printing (AJP)—an additive manufacturing (AM) process—are to: (1) realize in situ online monitoring of print quality in terms of line/electronic trace morphology; and (2) explain the causal aerodynamic interactions that govern line morphology based on a two-dimensional computational fluid dynamics (2D-CFD) model. To realize these objectives, an Optomec AJ-300 aerosol jet printer was instrumented with a charge coupled device (CCD) camera mounted coaxial to the nozzle (perpendicular to the platen). Experiments were conducted by varying two process parameters, namely, sheath gas flow rate (ShGFR) and carrier gas flow rate (CGFR). The morphology of the deposited lines was captured from the online CCD images. Subsequently, using a novel digital image processing method proposed in this study, six line morphology attributes were quantified. The quantified line morphology attributes are: (1) line width, (2) line density, (3) line edge quality/smoothness, (4) overspray (OS), (5) line discontinuity, and (6) internal connectivity. The experimentally observed line morphology trends as a function of ShGFR and CGFR were verified with computational fluid dynamics (CFD) simulations. The image-based line morphology quantifiers proposed in this work can be used for online detection of incipient process drifts, while the CFD model is valuable to ascertain the appropriate corrective action to bring the process back in control in case of a drift.

Hybrid additive manufacturing (hybrid-AM) has described hybrid processes and machines as well as multimaterial, multistructural, and multifunctional printing. The capabilities afforded by hybrid-AM are rewriting the design rules for materials and adding a new dimension in the design for additive manufacturing (AM) paradigm. This work primarily focuses on defining hybrid-AM in relation to hybrid manufacturing (HM) and classifying hybrid-AM processes. Hybrid-AM machines, materials, structures, and function are also discussed. Hybrid-AM processes are defined as the use of AM with one or more secondary processes or energy sources that are fully coupled and synergistically affect part quality, functionality, and/or process performance. Historically, defining HM processes centered on process improvement rather than improvements to part quality or performance; however, the primary goal for the majority of hybrid-AM processes is to improve part quality and part performance rather than improve processing. Hybrid-AM processes are typically a cyclic process chain and are distinguished from postprocessing operations that do not meet the fully coupled criterion. Secondary processes and energy sources include subtractive and transformative manufacturing technologies, such as machining, remelting, peening, rolling, and friction stir processing (FSP). As interest in hybrid-AM grows, new economic and sustainability tools are needed as well as sensing technologies that better facilitate hybrid processing. Hybrid-AM has ushered in the next evolutionary step in AM and has the potential to profoundly change the way goods are manufactured.

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