在制造系统中支持AI的AutoFactory数据集
Abderrahmane Boudribila1,2, Abdelouahed Tajer1, Zakaria Boulghasoul1
1Systems Engineering and Applications Laboratory, Cadi Ayyad University, ENSA, BP 2390, Marrakech, 40000, Marrakech-Safi, Morocco.
Data in brief
|August 21, 2025
概括
本研究介绍了AutoFactory,这是一个开源数据集,用于训练人工智能 (AI) 模型解释制造要求. 该数据集由大型语言模型 (LLM) 增强,并由专家验证,可用于自动化代码生成的准确组件识别.
科学领域:
- 工业自动化和人工智能
- 制造业自然语言处理 (NLP)
背景情况:
- 在制造业中自动化代码生成需要能够解释文本要求规范的AI模型.
- 一个重大挑战是缺乏公共可用的,特定领域的数据集来训练这些人工智能模型.
研究的目的:
- 提供AutoFactory,这是一个开源数据集,旨在培训制造业的AI模型.
- 为了从要求规范中提取和分类关键的工业部件.
主要方法:
- 开发了手写和LLM增强的要求规范的AutoFactory数据集.
- 用BIO格式进行命名实体识别 (NER) 的注释数据.
- 使用工厂I/O进行现实的场景,包括3D场景和多个摄像头视角.
- 使用大型语言模型增强语言多样性和语义相似性分析进行验证.
- 开发了一个定制的本地标签工具,用于用户友好和私有数据注释.
- 在数据集上的微调变压器模型 (BERT,DistilBERT).
主要成果:
- 自动工厂数据集可以提取和分类工业部件,如执行器,传感器和效应器.
- 通过手动验证,LLM增强实现了高语义相似性 (平均等号相似性> 0.92).
- 精心调整的模型在组件识别方面获得了高F1分值0.95.
- 证明了数据集在训练人工智能的有效性,以便从文本中解释制造行为.
结论:
- 自动工厂提供了一个强大的开源数据集,用于在制造代码中推进人工智能.
- 这一数据集有助于精确识别工业部件,减少手工编程的费用.
- 这项工作支持在工业自动化中自动生成控制代码的智能系统的开发.
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