| Abstract | Data-driven applications are penetrating every aspect of Additive Manufacturing (AM) to enable efficient resolution of key existing challenges. One such application is semantic segmentation which automatically quantifies post-process structure data from graphic or 3D computer representations. In industrial settings, these models can expedite the development of new materials or processes. However, Additively Manufactured (AMed) materials offer unique challenges, which require new models to be developed and trained. These challenges include out-of-balance classes (e.g., defects), massive labeling efforts, and significant data preparation costs. Recent applications of semantic segmentation in AM have shown the potential of ensemble-based approaches at the expense of increased parameters and computational burden while ignoring the minority classes. In this work, we propose a reproducible method and develop an associated tool to rapidly segment and subsequently quantify industrial AMed metallographic images. First, two new datasets representing AMed materials are generated from extensive experimentation. Subsequently, state-of-the-art models from convolutional and self-attention categories are evaluated in their ability to segment the datasets of interest. Finally, a modular software tool is developed for industrial applications. The developed Microstructure Segmentation, Quantification, and Fusion (+) tool or MicroSegQ+ enables a weighted pairing of predictions from different semantic segmentation models. The pairing strategy helps to exploit the complimentary performance of convolutional and transformer models on multi-class metallographic images from a Metal Matrix Composite (MMC) material system. In addition to developing models on this MMC dataset, the proposed strategy is evaluated on an open-source metal AM dataset. We also evaluate the generalizability of the best-performing model on a second MMC dataset from a different industrial setting that shows an overall accuracy of 93 % without fine-tuning. The proposed tool enables rapid quantification under 1 min, unlike the existing semi-automatic approach that takes 1–2 h of segmentation effort on single bead cross-sections. The tool is wrapped into a single executable file for industrial deployment. The work also contributes to two annotated datasets from a direct energy deposition (DED) AM process. The datasets can be used to develop and validate new semantic segmentation models of AMed microstructures, whereas the functionality of the tool can be expanded upstream to include data and modeling steps, enabling a no-code pipeline for the industry. |
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