Harnessing Large Language Models for Sustainable Materials and Manufacturing: Frameworks, Applications & Innovations

The integration of Large Language Models (LLMs) into materials and manufacturing offers a transformative approach to achieving ISO 9001 -compliant product quality, aligning with the goals of Industry 5.0. This work proposes MatManQ (Large Language Model in Material and Manufacturing for Product Quality and Control), a framework that leverages LLMs for quality control, assurance, and agentic AIdriven decision-making. A case study on alloy behavior using a self-curated dataset demonstrates the framework's capabilities. Five open-source LLMs-Mixtral-8x7B-327, TinyLlama-1.1B, deepset/roberta, Gemini, and FLAN-T5-were fine-tuned using a Retrieval-Augmented Generation (RAG) approach. Mixtral-8x7B-327 achieved the highest F1 score of $\mathbf{92.1\%}$, attributed to its sparse Mixture-of-Experts architecture, enabling precise and efficient reasoning. The study highlights the potential of LLMs to unify material and manufacturing insights for enhanced product quality, while outlining key challenges and future directions for industrial deployment.