A Review of Applications and Challenges of Large Language Models for Foundry Intelligence in the Casting Industry

Large language models (LLMs) and related foundation-model workflows are emerging as promising tools for advancing foundry intelligence across the casting value chain. This review examines their applications in material design and property prediction, process parameter optimization and intelligent control, and defect detection and quality tracing in casting environments. The surveyed studies indicate that LLM-enabled systems can help integrate unstructured technical knowledge with multimodal industrial data. This integration supports composition design, simulation-assisted process optimization, diagnostic reasoning, and knowledge-grounded decision support. However, current evidence shows that the transition from pilot demonstrations to robust industrial deployment remains constrained by several practical barriers, including heterogeneous data integration, insufficient traceability across process stages, reliability under physical and safety constraints, and the latency and resource limitations of shop-floor environments. We further highlight key research directions for real-world foundry applications, including multimodal cognitive systems, lightweight domain-adapted models, trustworthy retrieval-augmented and physics-aware reasoning, and human-in-the-loop validation frameworks. Overall, the review suggests that the future of foundry intelligence will depend not only on model capability, but also on data governance, deployable system design, and reliable integration with metallurgical knowledge and industrial workflows.