| contributor author | Wang, Peng | |
| contributor author | Fan, Zhaoyan | |
| contributor author | Kazmer, David O. | |
| contributor author | Gao, Robert X. | |
| date accessioned | 2017-11-25T07:17:56Z | |
| date available | 2017-11-25T07:17:56Z | |
| date copyright | 2017/24/8 | |
| date issued | 2017 | |
| identifier issn | 1087-1357 | |
| identifier other | manu_139_10_101008.pdf | |
| identifier uri | http://138.201.223.254:8080/yetl1/handle/yetl/4234849 | |
| description 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. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Orthogonal Analysis of Multisensor Data Fusion for Improved Quality Control | |
| type | Journal Paper | |
| journal volume | 139 | |
| journal issue | 10 | |
| journal title | Journal of Manufacturing Science and Engineering | |
| identifier doi | 10.1115/1.4036907 | |
| journal fristpage | 101008 | |
| journal lastpage | 101008-8 | |
| tree | Journal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 010 | |
| contenttype | Fulltext | |