Title page for ETD etd-03262012-185327

Type of Document Dissertation
Author Megahed, Fadel M.
Author's Email Address fmegahed@vt.edu
URN etd-03262012-185327
Title The Use of Image and Point Cloud Data in Statistical Process Control
Degree PhD
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Camelio, Jaime A. Committee Co-Chair
Woodall, William H. Committee Co-Chair
Ellis, Kimberly P. Committee Member
Nachlas, Joel A. Committee Member
  • and Spatiotemporal Analysis
  • Profile Monitoring
  • Machine Vision Systems
  • High Density Data
  • Fault Diagnosis
  • Control Charts
  • 3D Laser Scanners
Date of Defense 2012-03-26
Availability unrestricted
The volume of data acquired in production systems continues to expand. Emerging imaging technologies, such as machine vision systems (MVSs) and 3D surface scanners, diversify the types of data being collected, further pushing data collection beyond discrete dimensional data. These large and diverse datasets increase the challenge of extracting useful information. Unfortunately, industry still relies heavily on traditional quality methods that are limited to fault detection, which fails to consider important diagnostic information needed for process recovery. Modern measurement technologies should spur the transformation of statistical process control (SPC) to provide practitioners with additional diagnostic information. This dissertation focuses on how MVSs and 3D laser scanners can be further utilized to meet that goal. More specifically, this work: 1) reviews image-based control charts while highlighting their advantages and disadvantages; 2) integrates spatiotemporal methods with digital image processing to detect process faults and estimate their location, size, and time of occurrence; and 3) shows how point cloud data (3D laser scans) can be used to detect and locate unknown faults in complex geometries. Overall, the research goal is to create new quality control tools that utilize high density data available in manufacturing environments to generate knowledge that supports decision-making beyond just indicating the existence of a process issue. This allows industrial practitioners to have a rapid process recovery once a process issue has been detected, and consequently reduce the associated downtime.
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