Abstract
Multisensor data fusion can enable comprehensive representation of manufacturing processes, thereby contributing to improved part quality control. The effectiveness of data fusion depends on the nature of the input data. This paper investigates orthogonality as a measure for the effectiveness of data fusion, with the goal to maximize data correlation with part quality toward manufacturing process control. By decomposing sensor data into a lifted-dimensional space, contribution from each of the sensors for quantifying part quality is revealed by the corresponding projection vector. Performance evaluation using data measured from polymer injection molding confirmed the effectiveness of the developed technique.
| Original language | English |
|---|---|
| Article number | 101008 |
| Journal | Journal of Manufacturing Science and Engineering, Transactions of the ASME |
| Volume | 139 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 1 2017 |
Bibliographical note
Publisher Copyright:© 2017 by ASME.
Keywords
- accuracy
- data fusion
- measurement
ASJC Scopus subject areas
- Control and Systems Engineering
- Mechanical Engineering
- Computer Science Applications
- Industrial and Manufacturing Engineering