EAGER: Big Data Analytics for Advanced Manufacturing Improvement

EAGER：大数据分析促进先进制造改进

• 批准号: 1451088
• 负责人: Kamran Paynabar
• 金额: $ 30万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451088&HistoricalAwards=false
• 关键词: EAGER; Big; Data; Analytics; Advanced

Advanced manufacturing, is a group of manufacturing activities that use advanced technologies, cutting-edge materials, automation, and computation to manufacture new products and to improve manufacturing methods of existing products. Advanced manufacturing provides a capability to produce customized and complex products, while reducing both production time and cost. To ensure the quality and reliability of products, most advanced manufacturing systems are equipped with hundreds of sensors that are used to collect quality-related data such as the part dimensions, strength, etc. This results in a tremendous amount of data (namely, Big Data) that provide unique opportunities for improving advanced manufacturing systems. This EArly-Grant for Exploratory Research (EAGER) award supports fundamental research that enhances existing knowledge for the development of new analytical methods for Big Data that help improve the quality of manufactured products. The results of this research will have a significant impact on a variety of high profile application domains including automotive, aerospace, defense, biomedical, and medical by, preventing catastrophic failures, reducing scrap and rework costs, and improving product quality, all of which increase customer satisfaction and aid in economic growth. The use of statistical methods for improving manufacturing processes has been extensively studied and various techniques have been developed in this area. However, most existing methods fail to effectively analyze the big manufacturing process data due to their complex structure, high-volume, and high sampling rates. To address these issues, this research will propose and validate a new framework that enables modeling and analysis of Big Data in real-time by exploiting and integrating statistical learning, optimization, advanced computing, and manufacturing principles. The research team will develop a set of nonparametric methods for real-time sensing and measurement of Big Data, effective data compression, and process monitoring, change detection, and diagnosis.

Advanced Manufacturing是一组制造活动，使用高级技术，尖端材料，自动化和计算来制造新产品并改善现有产品的制造方法。高级制造提供了生产定制且复杂的产品的能力，同时减少了生产时间和成本。 为了确保产品的质量和可靠性，大多数先进的制造系统配备了数百种传感器，这些传感器用于收集与质量相关的数据，例如零件尺寸，强度等。这导致了大量数据（即大数据），这些数据（即大数据）为改善高级制造系统提供了独特的机会。这项探索性研究（急切）奖的早期赠款支持基础研究，从而增强了为大数据开发新的分析方法的现有知识，以帮助改善制成品的质量。这项研究的结果将对各种知名的应用领域产生重大影响，包括汽车，航空航天，国防，生物医学和医疗，防止灾难性故障，降低废料和返工成本以及提高产品质量，所有这些都会提高客户满意度和帮助经济增长。 已广泛研究了统计方法来改善制造过程的方法，并在该领域开发了各种技术。 但是，大多数现有方法由于其复杂的结构，大量和高采样率而无法有效地分析大型制造过程数据。为了解决这些问题，这项研究将提出和验证一个新框架，该框架可以通过利用和集成统计学习，优化，高级计算和制造原理来实时建模和分析大数据。研究团队将开发一组非参数方法，用于实时感测和测量大数据，有效的数据压缩以及过程监测，变更检测和诊断。