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From concept to manufacturing: evaluating vision-language models for engineering design

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Publicado en:The Artificial Intelligence Review vol. 58, no. 9 (Sep 2025), p. 288
Autor principal: Picard, Cyril
Otros Autores: Edwards, Kristen M., Doris, Anna C., Man, Brandon, Giannone, Giorgio, Alam, Md Ferdous, Ahmed, Faez
Publicado:
Springer Nature B.V.
Materias:
OpenAI
Language
Vision
Datasets
Engineering drawings
Large language models
Design engineering
Engineering education
Optimization
Medical research
Benchmarks
Natural language processing
Manufacturability
Manufacturing
Automation
Conceptual design
Topology optimization
Cognition & reasoning
Skills
Artifacts
Models
Engineering
Competence
Research design
Tasks
Language planning
Language shift
Language modeling
Research applications
Educational systems
Evaluation
Acceso en línea:Citation/Abstract
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Resumen:Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models have demonstrated impressive capabilities in enabling this shift. Yet, with text as their only input modality, they cannot leverage the large body of visual artifacts that engineers have used for centuries and are accustomed to. This gap is addressed with the release of multimodal vision-language models (VLMs), such as GPT-4V, enabling AI to impact many more types of tasks. Our work presents a comprehensive evaluation of VLMs across a spectrum of engineering design tasks, categorized into four main areas: Conceptual Design, System-Level and Detailed Design, Manufacturing and Inspection, and Engineering Education Tasks. Specifically in this paper, we assess the capabilities of two VLMs, GPT-4V and LLaVA 1.6 34B, in design tasks such as sketch similarity analysis, CAD generation, topology optimization, manufacturability assessment, and engineering textbook problems. Through this structured evaluation, we not only explore VLMs’ proficiency in handling complex design challenges but also identify their limitations in complex engineering design applications. Our research establishes a foundation for future assessments of vision language models. It also contributes a set of benchmark testing datasets, with more than 1000 queries, for ongoing advancements and applications in this field.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-025-11290-y
Fuente:ABI/INFORM Global