EAGER: Interpretable and Generalizable AI for Smart Manufacturing

EAGER：用于智能制造的可解释和可推广的人工智能

• 批准号: 2227450
• 负责人: Qi Zhao
• 金额: $ 25.18万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227450&HistoricalAwards=false
• 关键词: EAGER; Interpretable; Generalizable; AI; Smart

This EArly-concept Grant for Exploratory Research (EAGER) award is to conceptualize and research a generalized machine learning framework and the associated software tools needed to categorize manufacturing data acquired from a full-scale, operating commercial microelectronics fabrication facility and derive reliable control actions from that data using machine learning methods. Research on manufacturing-relevant machine learning methods has been frustrated by a lack of access to the large amount of industry-validated data needed to enable it. The project will explore the potential of new machine learning methods to reveal the implicit knowledge incorporated in that data to improve yield and productivity.The project addresses the three most critical impediments to the application of machine learning (ML) in manufacturing systems: (1) a lack of access to the massive amounts of data needed to research and develop machine learning architectures that are suited to manufacturing-derived data, (2) a lack of manufacturing-specific ML methods for aggregating and classifying that data to produce datasets tailored to training ML systems for specific processes, machines or operations and a lack of ML architectures that have been designed for and can make inferences using that data, and (3) a reluctance of manufacturing engineers to trust “black box” methods. The project is a collaboration with Seagate Technology to address all three impediments.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.

这项探索性研究（急切）奖的早期概念授予是概念化和研究广义的机器学习框架和相关的软件工具，以分类从全面运营的商业微电子制造设施中获取的制造数据，并使用机器学习方法从该数据中从该数据中得出可靠的控制措施。与制造业相关的机器学习方法的研究因缺乏访问启用它所需的大量行业验证数据而感到沮丧。该项目将探索新机器学习方法的潜力，以揭示该数据中纳入的隐含知识，以提高产量和生产力。该项目解决了在制造系统中应用机器学习（ML）应用的三个最关键的阻碍：（1）缺乏访问大量数据所需的大量数据，用于研究和开发用于制造数据的机器学习，以使其成为制造方法的群体（2）（2）制造方法（2）（2）（2）（2）制造方法（2）（2）该数据以生产针对特定流程，机器或操作的培训ML系统量身定制的数据集，以及缺乏为使用该数据设计并可以推断出的ML架构，（3）制造工程师不愿相信“黑匣子”方法。该项目是与Seagate Technology的合作，旨在解决这三个障碍。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估，被认为是珍贵的支持。

项目成果

Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning

• DOI: 10.48550/arxiv.2303.10482
• 发表时间: 2023-03
• 期刊: ArXiv
• 作者: Shi Chen;Qi Zhao