arXiv:2603.22017v2 Announce Type: replace Abstract: This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models (Gemma 3, Qwen 3, Gemma 4) using a relatively small dataset of around 50 million tokens. The dataset consists of open-access additive manufacturing journal articles with data extracted for the domain adaptive pretraining and visual instruction tuning processes. Various stages of the developed model are evaluated with the Additive-Manufacturing-Benchmark which consists of additive manufacturing dom
The proliferation of open-source large language models (LLMs) and increasing demand for specialized AI applications are leading to domain adaptation efforts that push the boundaries of AI utility beyond generalist models.
Domain-adapted LLMs for additive manufacturing indicate a growing trend of AI applications moving into highly specialized industrial processes, enabling significant efficiency gains and innovation in critical sectors.
This development shifts the paradigm from general-purpose AI to highly tailored AI systems, potentially accelerating the development cycles and sophistication of complex manufacturing techniques previously constrained by human expertise.
- · Additive Manufacturing Industry
- · AI Development Companies
- · Materials Science Researchers
- · Open-source AI Ecosystem
- · Traditional Manufacturing Processes
- · Companies without AI Adoption Strategies
Specialized AI models will significantly optimize and accelerate research and development in additive manufacturing.
This could lead to faster material innovation and more efficient production of highly complex components across various industries.
The widespread adoption of AI-driven additive manufacturing might reduce supply chain vulnerabilities and foster localized, advanced production capabilities globally.
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Read at arXiv cs.LG