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.
