QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Models
This addresses the need for trustworthy, interpretable quality assessment in additive manufacturing, particularly for reducing reliance on human operators, though it is incremental as it builds on existing VLMs with domain-specific enhancements.
The paper tackled the problem of black-box quality assessment in additive manufacturing by introducing QA-VLM, a framework using vision-language models to generate human-interpretable assessments, achieving higher validity and consistency in explanations on 24 laser wire DED samples compared to off-the-shelf VLMs.
Image-based quality assessment (QA) in additive manufacturing (AM) often relies heavily on the expertise and constant attention of skilled human operators. While machine learning and deep learning methods have been introduced to assist in this task, they typically provide black-box outputs without interpretable justifications, limiting their trust and adoption in real-world settings. In this work, we introduce a novel QA-VLM framework that leverages the attention mechanisms and reasoning capabilities of vision-language models (VLMs), enriched with application-specific knowledge distilled from peer-reviewed journal articles, to generate human-interpretable quality assessments. Evaluated on 24 single-bead samples produced by laser wire direct energy deposition (DED-LW), our framework demonstrates higher validity and consistency in explanation quality than off-the-shelf VLMs. These results highlight the potential of our approach to enable trustworthy, interpretable quality assessment in AM applications.