用大模型自动学制造工艺知识，小样本也能精准预测新工况。
Large Language Models for Extrapolative Modeling of Manufacturing Processes
- 从文献中自动提取工艺知识，结合少量实验数据迭代优化模型。
- 在小样本下外推性能显著优于传统机器学习方法。
- 适合缺乏专家经验或实验成本高的制造场景研究者使用。
制造过程的参数关系预测常受限于人为经验的主观性以及实验数据生成的成本与时间。本文提出一种新的大语言模型（LLM）框架，通过自动提取文献中嵌入的工艺相关知识，并基于少量实验数据进行模型迭代优化。该方法在基于切削、成形和增材制造的三个不同制造工艺上进行了评估。结果表明，在相同的小样本实验数据预算下，本框架生成的模型展现出意外的强外推能力，通常优于传统机器学习方法。此外，该方法无需人工构建初始模型或依赖专家解读文献。研究还揭示了从文献中提取知识的性质，以及知识提取与模型精炼两个环节的重要性。
原文摘要 · Abstract (English)
Conventional predictive modeling of parametric relationships in manufacturing processes is limited by the subjectivity of human expertise and intuition on the one hand and by the cost and time of experimental data generation on the other hand. This work addresses this issue by establishing a new Large Language Model (LLM) framework. The novelty lies in combining automatic extraction of process-relevant knowledge embedded in the literature with iterative model refinement based on a small amount of experimental data. This approach is evaluated on three distinct manufacturing processes that are based on machining, deformation, and additive principles. The results show that for the same small experimental data budget the models derived by our framework have unexpectedly high extrapolative performance, often surpassing the capabilities of conventional Machine Learning. Further, our approach eliminates manual generation of initial models or expertise-dependent interpretation of the literature. The results also reveal the importance of the nature of the knowledge extracted from the literature and the significance of both the knowledge extraction and model refinement components.
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