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research-article

Orthogonal analysis of multi-sensor data fusion for improved quality control

[+] Author and Article Information
Peng Wang

ASME Student Member, Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Glennan Bldg., 10900 Euclid Avenue, Cleveland, OH 44106-7222
pxw206@case.edu

Zhaoyan Fan

ASME Member, Department of Mechanical, Industrial and Manufacturing Engineering, Oregon State University, Dearborn Hall 217, Corvallis, OR 97331
zhaoyan.fan@oregonstate.edu

David Kazmer

ASME Fellow, Department of Plastics Engineering, University of Massachusetts Lowell, Ball 223, 219 Riverside St, Lowell, MA 01854
david_kazmer@uml.edu

Robert Gao

ASME Fellow, Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Glennan Bldg., 10900 Euclid Avenue, Cleveland, OH 44106-7222
robert.gao@case.edu

1Corresponding author.

ASME doi:10.1115/1.4036907 History: Received January 31, 2017; Revised May 19, 2017

Abstract

Multi-sensor data fusion enables more comprehensive representation of the physical process being monitored to improve manufacturing consistency and productivity. The effectiveness of data fusion, however, is dependent upon the type of the data being fused. This paper investigates orthogonality as a measure for the effectiveness of data fusion, with the goal to maximize its correlation with manufactured part quality for process control. By decomposing sensor data into a lifted-dimensional space, the contribution from each sensor to quantification of part quality is revealed by the corresponding projection vector. The performance of the method and the uncertainty involved are evaluated using experimental data from precision injection molding.

Copyright (c) 2017 by ASME
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