| Abstract Scope |
Powder characterization is key to the efficient optimization of additive manufacturing techniques such as cold spray. To accomplish this, computer vision has proven to be effective at predicting and segmenting cold spray powder from SEM micrographs. However, while good at identifying and analyzing powders, outputs are large, complex, and difficult to analyze. This work presents a novel post-processing step utilizing a large language model (LLM) to interface with these outputs, making them more interpretable for real-world applications. This framework can process and analyze these results, answering practical questions about various morphological characteristics, such as area, eccentricity, sphericity, etc. Additionally, this approach can be used to compare multiple powders, enabling the user to query which characteristics are most similar or most different between samples. By embedding computer vision results in a readable format, an LLM improves the accessibility and interpretability of computer vision results for powder characterization. |