用大模型预测3D打印缺陷，准确率达93%。
AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing
- 用工艺参数微调大模型，实现缺陷预测。
- 在稀疏数据集上准确率93%，支持自然语言输入。
- 适合制造业用户快速优化打印参数。
本文研究大语言模型根据工艺参数输入预测增材制造缺陷类型的能力。基于工艺参数-缺陷数据集，我们微调了一组模型，命名为AdditiveLLM，用于预测关键孔、未熔合和球化等缺陷模式。通过对比不同输入格式，评估模型在稀疏基准数据集和自然语言提示数据集上的表现。模型展现出强健的预测能力，在给定工艺参数时正确识别缺陷模式的准确率达到93%。引入自然语言输入进一步简化了参数选择流程，使用户能针对特定构建需求快速定位最优工艺设置。
原文摘要 · Abstract (English)
In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize a process parameter defect dataset to fine-tune a collection of models, titled AdditiveLLM, for the purpose of predicting potential defect regimes including Keyholing, Lack of Fusion, and Balling. We compare different methods of input formatting in order to gauge the model's performance to correctly predict defect regimes on our sparse Baseline dataset and our natural language Prompt dataset. The model displays robust predictive capability, achieving an accuracy of 93\% when asked to provide the defect regimes associated with a set of process parameters. The incorporation of natural language input further simplifies the task of process parameters selection, enabling users to identify optimal settings specific to their build.
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