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学术论坛报告:史建军 院士——Evolution of Data Fusion for Quality Improvements: Past, Current, and Future

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学术论坛通知:本期学术论坛由佐治亚理工机构的史建军院士主讲

题目:Evolution of Data Fusion for Quality Improvements: Past, Current, and Future

报告人:史建军 教授 (美国工程院(NAE)院士)

时间: 2019112日,14:00-15:30

地点:机械楼南高厅

  

报告摘要:

This is a keynote talk given at the INFORMS when it celebrates 20 years anniversary of the QSR Section.  In this talk, I will provide a review of the history of data fusion R&D efforts for quality improvements in the past 20+ years.  Data Fusion research can be traced back for decades, but systematic data fusion research really started in 1995 with the promotion of “In-Process Quality Improvements”, which emphasizes the fusion of product/process design data, in-process sensing data, and quality measurements to achieve early fault detection, root cause diagnosis, and defect prevention.  The data fusion research was later extended for modeling and analysis of Stream of Variations in multistage manufacturing systems, addressing fundamental issues in variation modeling, tolerance synthesis, distributed sensing, root cause diagnosis, and error compensation for system variation reduction. With the advancements in data science, machine learning, and computing capabilities, data fusion research has stepped into to a new era, tackling the challenges brought by high dimensional streaming data, heterogeneous data, cyber-physical networks, and the need of physics-guided machine learning for quality improvements.  All these data fusion R&D efforts focus on fundamental research for solving problems motived by real engineering needs. Methods adopted or developed are interdisciplinary, integrating engineering domain knowledge, data science, and decision analytics and control.  This talk provides a summary of major milestones in data fusion research, how it evolved, and where it was implemented.  Future data fusion research directions will be discussed as well.

  

报告人简介:

史建军博士是佐治亚理工机构工业与系统工程机构和巴黎人娱乐blr0022:联合聘任的Carolyn J. Stewart 讲席教授。他曾是密歇根大学工程机构G. Lawton and Louise G. Johnson Professor讲席教授。史教授分别在1984年和1987年于北京理工大学获自动化工程本科、硕士学位,1992年于密歇根大学获机械工程博士学位。史建军教授研究领域是工业大数据、智能制造、质量科学和工业工程。他累计发表论文180多篇,获得科研总经费2000多万美元。在研究领域,史教授强调融合工程知识、高等统计、系统信息化和控制理论等开发创新性的理论和方法,为制造和服务产品设计、质量控制和系统改善奠定了基础。史教授建立的变异流理论、多层次结构模型和数据融合分析方法等,为多工序制造过程的误差分析和质量控制提供了一套全新的理论体系,改变了过去只能针对各个工序孤立进行误差分析和事后被动的缺陷检测的传统做法,被美国工业企业广泛应用。史教授还是美国质量统计与可靠性学会发起人,是美国工业与系统工程学会旗舰刊物IISE Transactions的主编。史教授出色的教育和科研成果为他赢得了NSF CAREER奖、IISE David F. Baker 杰出科研奖, IIE Albert G. Holzman 杰出教育奖等重大奖项,并当选为美国工业与系统工程学会(IISE)会士、美国机械工程学会(ASME)会士,美国运筹与管理科学学会(INFORMS)的会士,国际质量科机构(IAQ)院士美国工程院(NAE)院士

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