NSF I/UCRC in Quality and Reliability Engineering

罗格斯大学 质量和可靠性工程是保持美国制造能力有效性所必需的一个基本要素。 由于这一领域对美国工业的重要性，罗格斯大学和亚利桑那州立大学（包括亚利桑那州立大学）获得了EEC-9526413计划拨款，以探索建立一个质量和可靠性工程的工业/大学合作研究中心。 1996年7月举行的一次规划会议的结果是，获得了足够的工业支持，成为一个工业/大学合作研究中心（I/UCRC）。 该中心的初步研究议程将解决：改进产品，是由客观测量和消费者小组数据判断;最佳设计质量使用的声音客户;系统可靠性预测与不确定性的组件可靠性估计;整合贝叶斯和物理故障的方法，以获得测试时间压缩;一个通用的可靠性预测模型，加速寿命测试;电子制造的加速测试;多元统计工具的数据挖掘;设备和过程表征、优化和鉴定;多元过程监控;汽车系统的软件可靠性和开发过程模型;过程能力指数和损失函数。 该建议已与Geogia-Ann Klutke博士和Lawrence Seiford博士协调，他们是NSF运筹学和生产系统计划的项目主任。

ABSTRACT EEC-9713003 ELSAYED Rutgers University Quality and reliability engineering is an essential ingredient required to maintain the United States manufacturing capacity effectiveness. Because of the importance of this area to U.S. industry, a planning grant, EEC-9526413, was awarded to Rutgers University and Arizona State University (including the Arizona State University) to explore the forming of an Industry/University Cooperative Research Center for Quality and Reliability Engineering. As a result of a planning meeting held in July 1996, sufficient industrial support has been generated to become an Industry/University Cooperative Research Center (I/UCRC). The Center's initial research agenda will address: Improving Products that are Judged by Objective Measurements and Consumer Panel Data; Optimal Design for Quality Using the Voice of the Customer; System Reliability Prediction with Uncertainty in Component Reliability Estimates; Integration of Bayesian and Physics of Failure Approaches to Obtain Test Time Compression; A General Reliability Prediction Model for Accelerated Life Testing; Accelerated Testing for Electronics Manufacturing; Data Mining with Multivariate Statistical Tools; Equipment and Process Characterization, Optimization, and Qualification; Multivariate Process Monitoring and Control; Software Reliability and Development Process Models for Automobile Systems; Process Capability Indices and Loss Function. This proposal has been coordinated with Drs. Geogia-Ann Klutke and Lawrence Seiford, NSF Program Directors for the Operations Research and Production Systems Program.