This research develops a novel Large Language Model (LLM) framework that transforms how we predict manufacturing process parameters with minimal experimental data.

• Combines automatic extraction of process knowledge from literature with iterative model refinement
• Reduces dependency on extensive experimental data collection
• Minimizes human subjectivity in manufacturing predictions
• Achieves accurate modeling through knowledge-guided extrapolation

For engineering teams, this approach significantly reduces the time and cost of manufacturing process optimization while maintaining accuracy, enabling faster innovation cycles and more efficient production setup.

Large Language Models for Extrapolative Modeling of Manufacturing Processes