mlsanityData to 14 Oct 2026

QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Models

arXiv 2508.16661 ↗First posted 20 Aug 2025cs.CVcited by 1

On the map: Multimodal LLMs / vision-language models → Chart understanding and anomaly detection

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 6 of its 29 references that are in the map. First posted on arXiv 20 Aug 2025.

Builds on

6 of 29 references.

References · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 130
  4. 2024Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiescited by 69
  5. 2024AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturingcited by 23
  6. 2024LLM-3D Print: Large Language Models To Monitor and Control 3D Printingcited by 18

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Employing Vision-Language Models for Face Image Quality Assessmentcited by 0

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  3. 2025Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models
  4. 2025Vision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Description
  5. 2026FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
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  7. 2024Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs
  8. 2025An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing