Data Quality in Manufacturing Industrial Internet Integration

制造业工业互联网集成中的数据质量

• 批准号: 2331985
• 负责人: Ran Jin
• 金额: $ 30万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331985&HistoricalAwards=false
• 关键词: Data; Quality; Manufacturing; Industrial; Internet

This research project is focused on data quality evaluation and improvement for effective Artificial Intelligence (AI) deployment in manufacturing. In modern manufacturing industries, AI-guided decision-making has revolutionized production, product quality, design customization, and manufacturing sustainability. However, the lack of quantitative data quality evaluation and effective data preparation methodologies for manufacturing AI models pose a few critical challenges, including untrustworthy AI decision-making, high energy consumptions to process large but quality-poor datasets, and a lack of more effective datasets to be shared across manufacturing systems for AI model training. These challenges greatly slow down the adoption of AI technologies in manufacturing industries, thus significantly impacting the global competitiveness of US manufacturing. This research project defines and evaluates quantitative manufacturing data quality metrics, advances scientific knowledge for data quality assurance based on manufacturing features, and promotes dataset preparation for AI modeling. As a result, the research not only enables fast training and comparison of AI models due to improved manufacturing data quality, but also reduces environmental impact on data computation, communication, and storage. This research project also includes a comprehensive outreach and education program for college students and manufacturing workforce development, including panel discussion, outreach seminars to underrepresented students and practitioners, and manufacturing AI competitions. The goal of this research project is to define, evaluate, and improve data quality to enable compatible usage of datasets in Manufacturing Industrial Internet integrated by heterogenous machines, sensors, and computation devices. The project builds the data quality methodology to address the challenges based on manufacturing specific data format and modalities from different manufacturing layouts. First, the data quality is defined as inversely proportional to the variance of AI model performance. A latent neural recommender system investigates the interface between datasets and AI models to assess data quality when different AI models are used. Second, manufacturing data quality is modeled based on the unique manufacturing data features from graphs of different manufacturing layouts and data modalities. Third, after the root causes of poor data quality are identified, golden datasets are generated by ensemble active learning by contextual bandits to ensure robust manufacturing AI model performance to data source variabilities. The data quality methodology connects to the manufacturing hierarchical variable relationship, multimodal data, and layout representations with effective feature representations. Methodologies are validated by both real datasets in Semiconductor Manufacturing and a Manufacturing Industrial Internet testbed.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目的重点是数据质量评估和有效人工智能（AI）部署的改进。在现代制造行业中，AI引导的决策彻底改变了生产，产品质量，设计定制和制造可持续性。但是，缺乏用于制造AI模型的定量数据质量评估和有效的数据准备方法构成了一些关键挑战，包括不信任的AI决策，高能量消耗来处理大型但优质贫困的数据集，以及缺乏在AI模型培训的制造系统中共享的更有效的数据集。这些挑战极大地降低了AI技术在制造业中的采用，从而显着影响了美国制造业的全球竞争力。 该研究项目定义和评估定量制造数据质量指标，基于制造功能的数据质量保证的科学知识，并促进数据集准备AI建模。 结果，该研究不仅可以通过改进的制造数据质量来快速培训和比较AI模型，还可以减少对数据计算，通信和存储的环境影响。该研究项目还包括一个针对大学生和制造业劳动力发展的综合外展和教育计划，包括小组讨论，向代表性不足的学生和从业人员进行推广研讨会以及制造AI竞赛。 该研究项目的目的是定义，评估和改善数据质量，以使数据集在由异源机器，传感器和计算设备集成的制造工业互联网中兼容。该项目构建了数据质量方法，以应对基于制造特定数据格式的挑战和不同制造布局的方式。首先，数据质量定义为与AI模型性能的差异成反比。潜在的神经推荐系统研究数据集和AI模型之间的接口，以评估使用不同的AI模型时的数据质量。其次，制造数据质量是根据不同制造布局和数据模式图的独特制造数据特征建模的。第三，在确定了数据质量差的根本原因之后，通过通过上下文匪徒进行集合的主动学习生成金数据集，以确保对数据源变异性的强大制造AI模型性能。数据质量方法与具有有效特征表示形式的制造层次变量关系，多模式数据和布局表示相关。方法论通过半导体制造中的真实数据集和制造业工业互联网测试床都得到了验证。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。