Designing and printing metamaterials with customizable architectures enables the realization of unprecedented mechanical behaviors that transcend those of their constituent materials. These behaviors are recorded in the form of response curves, with stress-strain curves describing their quasi-static footprint. However, existing inverse design approaches are yet matured to capture the full desired behaviors due to challenges stemmed from multiple design objectives, nonlinear behavior, and process-dependent manufacturing errors. Here, we report a rapid inverse design methodology, leveraging generative machine learning and desktop additive manufacturing, which enables the creation of nearly all possible uniaxial compressive stress‒strain curve cases while accounting for process-dependent errors from printing. Results show that mechanical behavior with full tailorability can be achieved with nearly 90% fidelity between target and experimentally measured results. Our approach represents a starting point to inverse design materials that meet prescribed yet complex behaviors and potentially bypasses iterative design-manufacturing cycles.

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%.

Supervised machine learning techniques, such as classification models, have been widely applied to online process anomaly detection in advanced manufacturing. However, since abnormal process states rarely occur in regular manufacturing settings, the data collected for model training may be highly imbalanced, which may result in significant training bias for supervised learning and, thus, further deteriorate the anomaly detection accuracy. To reduce the training bias, a natural idea is to incorporate data augmentation techniques to generate effective artificial sample data for the abnormal process states. However, most of the existing data augmentation methods do not effectively consider the temporal orders of the sensor signals, and they also usually require large amounts of actual samples to ensure satisfactory augmentation performance. To address these limitations, this article developed a novel data-driven methodology termed augmented time regularized generative adversarial network (ATR-GAN). By incorporating a proposed augmented generator, ATR-GAN is capable of generating more effective artificial samples for training supervised learning models. The novelty of this augmented generator in the proposed methodology can be summarized into three aspects: 1) an augmented filter layer is introduced in the augmented generator to identify the high-quality artificial samples; 2) in the augmented filter layer, a new distance metric termed time-regularized Hausdorff (TRH) distance is developed to accurately measure the similarity between actual samples and the generated artificial samples; and 3) batching techniques are also employed in the proposed augmented generator to further increase the diversity of the artificial data and fully utilize the relatively limited training data. In addition, the effectiveness of the proposed ATR-GAN is also validated by both numerical simulation and a real-world case study in additive manufacturing.