SBIR Phase I: Agent-Based Identification of Constitutive Relationships from Large Manufacturing Datasets

SBIR 第一阶段：基于主体的大型制造数据集本构关系识别

• 批准号: 2111638
• 负责人: Branden Kappes
• 金额: $ 25.6万
• 依托单位: CONTEXTUALIZE, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2111638&HistoricalAwards=false
• 关键词: SBIR; Phase; Agent; Based; Identification

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve utilization of data resources. While data is an integral part of contemporary business—used to inform strategic, technical, and financial decisions—data collection remains federated in many fields, including manufacturing, because logistical, practical, and strategic hurdles prevent centralization. Consequently, these data resources quickly become isolated. No longer FAIR (Findable, Accessible, Interoperable, Reusable), the value of data so expensive to collect is lost. The proposed technology addresses two major concerns facing effective utilization of federated data. First, it develops a unified interface to analyze and explore federated data, without sacrificing control over data access. Second, it integrates machine learning with an understanding of the physical system. The proposed technology is a mathematically rigorous translation between neural networks and the constitutive relationships describing the underlying physics. The two approaches will leverage measurements of a process environment, including time, temperature, and pressure, as well as mechanical strength or chemical reactivity. Neural networks, which are general and easy to train, estimate system behavior through statistical correlations, which is ideal for repetitive, complex systems, such as manufacturing processes; but they require increasingly large and diverse datasets to expand the conditions under which they are reliable. In contrast, constitutive relationships, which often take years to develop, can be used to predict how a system will behave under new conditions. This system will integrate both approaches.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.

小企业创新研究（SBIR）第一阶段项目的更广泛影响是提高数据资源的利用率。虽然数据是当代商业不可或缺的一部分，用于为战略、技术和财务决策提供信息，但数据收集在许多领域（包括制造业）仍然是联合的，因为物流、实践和战略障碍阻碍了集中化。因此，这些数据资源很快就会被隔离。不再公平（可查找，可扩展，可互操作，可重用），数据的价值如此昂贵的收集丢失。所提出的技术解决了有效利用联邦数据所面临的两个主要问题。首先，它开发了一个统一的接口来分析和探索联邦数据，而不会牺牲对数据访问的控制。其次，它将机器学习与对物理系统的理解相结合。 所提出的技术是神经网络和描述底层物理的本构关系之间的数学严格转换。这两种方法将利用对过程环境的测量，包括时间、温度和压力，以及机械强度或化学反应性。神经网络是通用的，易于训练，通过统计相关性来估计系统行为，这对于重复的复杂系统（如制造过程）是理想的;但它们需要越来越大和多样化的数据集来扩展它们可靠的条件。相比之下，本构关系，往往需要数年的发展，可以用来预测一个系统将如何在新的条件下表现。 该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。