Modeling Data from Electronics Manufacturing Processes

对电子制造过程中的数据进行建模

• 批准号: 0072960
• 负责人: Jye-Chyi Lu
• 金额: $ 5.28万
• 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: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0072960&HistoricalAwards=false
• 关键词: Modeling; Data; Electronics; Manufacturing; Processes

The goal of this university-industry cooperative research project is to create an environment for training students with experience of solving real-life application problems. The proposed technical projects have potentials to make a broad impact in analyzing large volumes of (non-standard) data in many engineering applications. Both NSF and Siemens provide half of the three student research assistantships, and Siemens also provides engineering expertise, process data and facility for experimentation. Half of the student time will be spent on understanding the product, process, problem and data encountered at Siemens and the other half of the student time will be involved in developing general methodologies that solve problems similar to what Siemens encountered.With more advanced tools developed in automating the data collection process, many companies like Siemens have an opportunity to explore ways to extract knowledge from this data for improving their decision-making quality. The challenges in synthesizing data collected from Siemens' printed circuits board assembly processes are the large sizes of data and the numerous numbers of zero-defect counts. This proposal formulates several specific tasks outlined as follows to solve these problems: (1) Extract mean and variance change-point features (using data segmentation and wavelet procedures), (2) Formulate multi-layer process improvement rules, (3) Research automatic data modeling procedures, (4) Conduct Bayesian analysis of zero-inflated data for improving production quality.There are many opportunities for developing statistical theory to understand the proposed methods, and all tasks involve heavy computing and data analysis. Siemens has located trial-out data for students to gain hands-on experience in their research training programs. This proposal first formulated a task-force team consisting of university faculty members, students and company managers for organizing our resources and efforts to develop mathematical tools in the proposed technical projects. The committee will then identify short- and long-term goals and formulate specific problems for student thesis research training. Finally, students will conduct hands-on projects, document study results and present findings in meetings. This project is jointly supported by the MPS Office of Multidisciplinary Activities (OMA) and the Division of Mathematical Sciences.

本大学与产业合作研究项目的目标是为培养具有解决实际应用问题经验的学生创造一个环境。拟议的技术项目有可能在许多工程应用中分析大量（非标准）数据方面产生广泛的影响。 NSF和西门子提供三个学生研究助学金的一半，西门子还提供工程专业知识，工艺数据和实验设施。学生的一半时间将用于了解西门子的产品、流程、问题和遇到的数据，另一半时间将用于开发通用方法，以解决与西门子遇到的问题类似的问题。随着更先进的工具在自动化数据收集过程中的开发，像西门子这样的许多公司有机会探索从这些数据中提取知识的方法，以提高他们的决策质量。西门子印刷电路板组装过程中收集的数据的合成面临的挑战是数据量大和零缺陷计数的数量众多。 为解决这些问题，该提案提出了以下几项具体任务：（1）提取均值和方差变点特征（采用数据分割和小波程序），（2）制定多层过程改进规则，（3）研究自动数据建模程序，（4）进行贝叶斯分析，膨胀数据以提高生产质量。有很多机会发展统计理论来理解所提出的方法，所有任务都涉及繁重的计算和数据分析。 西门子为学生提供了试验数据，以便在他们的研究培训计划中获得实践经验。该提案首先组建了一个由大学教师、学生和公司经理组成的工作组，以组织我们的资源和努力，在拟议的技术项目中开发数学工具。 然后，委员会将确定短期和长期目标，并制定学生论文研究培训的具体问题。 最后，学生将进行实践项目，记录研究结果，并在会议上提出调查结果。 该项目由MPS多学科活动办公室（OMA）和数学科学部共同支持。