Date of Award
Doctor of Philosophy
Industrial and Entrepreneurial Engineering and Engineering Management
Dr. Lee J. Wells
Dr. Steven Butt
Dr. Azim Houshyar
Dr. Mitchel Keil
High-density data, quality control, 3D laser scanners, spatial statistics, statistical process control, gage study
In modern manufacturing, advanced metrology systems are continually being incorporated into quality control (QC) systems to provide high-density (HD) datasets. These datasets can contain millions of measurements that can be used to represent a part’s whole geometry. While integrating HD datasets into QC systems has brought several opportunities to enhance the performance of QC systems, it has resulted in new challenges in this area as well. While significant amounts of research have been performed in this area, the QC research community still strives to tackle these challenges. This study identifies key challenges regarding incorporating HD datasets into QC systems. Specifically, three unique challenges identified from extensive literature review and the proposed solutions have been presented as a Research Effort I, II and III. The focuses of these research efforts are mainly on a) Developing a Gage study framework for HD data (e.g. point clouds), b) Developing cost-efficient monitoring scheme for monitoring of HD fused datasets, and c) Assessing the performance of different control chart approaches to detect multiple shift types in presence of HD data. The goal of this study is to overcome these challenges by developing QC tools for more effective and efficient use of HD datasets.
Dastoorian, Romina, "Advancing the Use of Quality Control Tools for High-Density Data in Modern Manufacturing" (2021). Dissertations. 3711.