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  1. In review.
  4. The widespread growth of additive manufacturing, a field with a complex informatic “digital thread”, has helped fuel the creation of design repositories, where multiple users can upload distribute, and download a variety of candidate designs for a variety of situations. Additionally, advancements in additive manufacturing process development, design frameworks, and simulation are increasing what is possible to fabricate with AM, further growing the richness of such repositories. Machine learning offers new opportunities to combine these design repository components’ rich geometric data with their associated process and performance data to train predictive models capable of automatically assessing build metrics related tomore »AM part manufacturability. Although design repositories that can be used to train these machine learning constructs are expanding, our understanding of what makes a particular design repository useful as a machine learning training dataset is minimal. In this study we use a metamodel to predict the extent to which individual design repositories can train accurate convolutional neural networks. To facilitate the creation and refinement of this metamodel, we constructed a large artificial design repository, and subsequently split it into sub-repositories. We then analyzed metadata regarding the size, complexity, and diversity of the sub-repositories for use as independent variables predicting accuracy and the required training computational effort for training convolutional neural networks. The networks each predict one of three additive manufacturing build metrics: (1) part mass, (2) support material mass, and (3) build time. Our results suggest that metamodels predicting the convolutional neural network coefficient of determination, as opposed to computational effort, were most accurate. Moreover, the size of a design repository, the average complexity of its constituent designs, and the average and spread of design spatial diversity were the best predictors of convolutional neural network accuracy.« less
  5. Intricate mesostructures in additive manufacturing (AM) designs can offer enhanced strength-to-weight performance. However, complex mesostructures can also hinder designers, often resulting in unpalatably large digital files that are difficult to modify. Similarly, existing methods for defining and representing complex mesostructures are highly variable, which further increases the challenge in realizing such structures for AM. To address these gaps, we propose a standardized framework for designing and representing mesostructured components tailored to AM. Our method uses a parametric language to describe complex patterns, defined by a combination of macrostructural, mesostructural, and vector field information. We show how various mesostructures, ranging frommore »simple rectilinear patterns to complex, vector field-driven cellular cutouts can be represented using few parameters (unit cell dimensions, orientation, and spacing). Our proposed framework has the potential to significantly reduce file size, while its extensible nature enables it to be expanded in the future.« less