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U.S. - Germany Cooperative Research: Integration of Statistical and Automatic Control Techniques for Economic Quality Control

U.S. - Germany Cooperative Research: Integration of Statistical and Automatic Control Techniques for Economic Quality Control
美德合作研究:统计与自动控制技术的整合用于经济质量控制
批准号:
9513444
负责人:
Enrique Del Castillo
金额:
$0.6万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1996
资助国家:
美国
项目状态:
已结题
起止时间:
1996-04-15 至 1998-03-31

项目摘要

项目成果

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中文摘要
翻译
该奖项支持德克萨斯大学阿灵顿分校的Enrique Del Castillo教授与德国维尔茨堡大学的Elart von Collani教授在质量工程方面的合作。他们的研究目标是开发过程控制程序,将统计过程控制和自动(或工程)过程控制技术相结合,以确保制造过程的质量。Del Castillo博士为这项联合工作带来了与包含过程动力学的随机模型合作的优势。冯·科拉尼博士在制造过程的经济建模和质量控制的相关统计抽样程序方面的专门知识对此起到了补充作用。这项研究将有助于一个新兴的工业研究领域:工程控制理论原理在质量控制领域的应用。它应该会导致新一代受经济影响的短期制造系统的过程控制程序。
英文摘要
This award supports Professor Enrique Del Castillo of the University of Texas at Arlington to collaborate in Quality Engineering with Professor Elart von Collani of the University of Wuerzburg, Germany. The objective of their research is to develop process control procedures that integrate statistical process control and automatic (or engineering) process control techniques for the quality assurance of manufacturing processes. Dr. Del Castillo brings to this joint effort strengths in working with stochastic models that incorporate process dynamics. This is complemented by Dr. von Collani's expertise in the economic modelling of manufacturing processes and in the relevant statistical sampling procedures for quality control. This research will contribute to an emerging field of industrial research: the application of engineering control theory principles to the area of quality control. It should result in a new generation of economically influenced process control procedures for short-run manufacturing systems.
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会议论文
Deep Intrinsic Learning for On-line Process Control of Manufacturing Manifold Data
High Dimensional Statistical Inference in Flexible Response Surface Models for Product Formulation
Collaborative Research: Active Statistical Learning: Ensembles, Manifolds, and Optimal Experimental Design
On-line Profile-to-Profile Process Adjustment for Robust Parameter Design Scenarios
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