CAREER: Transfer Learning Based Quality Improvement in Spatially-Temporally Complex Systems

职业：时空复杂系统中基于迁移学习的质量改进

• 批准号: 1149602
• 负责人: Jing Li
• 金额: $ 40万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149602&HistoricalAwards=false
• 关键词: CAREER; Transfer; Learning; Based; Quality

The research objective of this Faculty Early Career Development (CAREER) award is to develop "transfer learning" based methodologies for quality improvement of manufacturing systems featured by a high product variety and short life cycles (called spatially-temporally complex systems). Such systems typically exist in semiconductor and renewable-energy manufacturing industries. The methodological innovation - transfer learning - refers to the capability of leveraging the knowledge gained during quality control of one process (or past generations of a process) for quality control of other processes (or a new generation). A body of statistically rigorous and computationally efficient transfer learning based methods will be developed to achieve various quality control objectives such as process modeling, monitoring, root cause diagnosis, sensor uncertainty modeling, and sensor placement. Integrated with the research is an ambitious education plan aiming at equipping future workforce with new science and engineering knowledge and associated skills in quality improvement. If successful, the results of this research will significantly expedite the learning curve in quality improvement of each process in a manufacturing system through effective knowledge transfer from other processes and past generations. This will enable robust, real-time and even proactive quality control decision making to keep up with rapid product proliferation and generation changes. Through validation and application in semiconductor and solar energy manufacturing, this research will provide breakthrough technologies to significantly improve the quality, productivity, and cost-effectiveness of these industries. The research methodologies are also transferrable to the study of other systems such as health care delivery, human brain, and biological systems. A broad array of educational activities will be pursued, including new course development, industrial training sessions, undergraduate and minority students involvement, and K-12 students and teachers outreach. Research and education programs will be established through strong international collaboration to achieve global impact.

该学院早期职业发展（CAREER）奖的研究目标是开发基于“迁移学习”的方法，用于提高具有高产品多样性和短生命周期的制造系统（称为时空复杂系统）的质量。这种系统通常存在于半导体和可再生能源制造行业。方法创新-迁移学习-是指利用在一个过程（或一个过程的过去几代）的质量控制过程中获得的知识进行其他过程（或新一代）质量控制的能力。将开发一系列统计上严格和计算上有效的基于迁移学习的方法，以实现各种质量控制目标，如过程建模、监控、根本原因诊断、传感器不确定性建模和传感器放置。 与研究相结合的是一项雄心勃勃的教育计划，旨在为未来的劳动力提供新的科学和工程知识以及质量改进的相关技能。如果成功，本研究的结果将显着加快学习曲线，在质量改进的每个过程中的制造系统，通过有效的知识转移，从其他过程和过去的几代人。这将使强大的，实时的，甚至主动的质量控制决策，以跟上快速的产品扩散和一代的变化。通过在半导体和太阳能制造中的验证和应用，这项研究将提供突破性技术，以显着提高这些行业的质量，生产率和成本效益。研究方法也可转移到其他系统的研究，如医疗保健提供，人脑和生物系统。将开展广泛的教育活动，包括新课程开发，工业培训课程，本科生和少数民族学生的参与，以及K-12学生和教师的推广。 研究和教育计划将通过强有力的国际合作建立，以实现全球影响。