Research Papers

Orthogonal Analysis of Multisensor Data Fusion for Improved Quality Control

[+] Author and Article Information
Peng Wang

Department of Mechanical and
Aerospace Engineering,
Case Western Reserve University,
Glennan Building, 10900 Euclid Avenue,
Cleveland, OH 44106-7222
e-mail: Pxw206@case.edu

Zhaoyan Fan

Department of Mechanical,
Industrial and Manufacturing Engineering,
Oregon State University,
Dearborn Hall 217,
Corvallis, OR 97331
e-mail: Zhaoyan.Fan@oregonstate.edu

David O. Kazmer

Fellow ASME
Department of Plastics Engineering,
University of Massachusetts Lowell,
Ball 223, 219 Riverside Street,
Lowell, MA 01854
e-mail: David_Kazmer@uml.edu

Robert X. Gao

Fellow ASME
Department of Mechanical and
Aerospace Engineering,
Case Western Reserve University,
Glennan Building, 10900 Euclid Avenue,
Cleveland, OH 44106-7222
e-mail: Robert.Gao@case.edu

Manuscript received January 31, 2017; final manuscript received May 19, 2017; published online August 24, 2017. Assoc. Editor: Ivan Selesnick.

J. Manuf. Sci. Eng 139(10), 101008 (Aug 24, 2017) (8 pages) Paper No: MANU-17-1065; doi: 10.1115/1.4036907 History: Received January 31, 2017; Revised May 19, 2017

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.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.


Chong, C. Y. , Mori, S. , Chang, K. C. , and Barker, W. H. , 2000, “ Architecture and Algorithms for Track Association and Fusion,” IEEE Aerosp. Electron. Syst., 15(1), pp. 5–13. [CrossRef]
Luo, R. C. , Chang, C. C. , and Lai, C. C. , 2011, “ Multisensor Fusion and Integration: Theories, Application, and Its Perspectives,” IEEE Sens. J., 11(12), pp. 3123–3138. [CrossRef]
Liu, K. , and Huang, S. , 2016, “ Integration of Data Fusion Methodology and Degradation Modeling Process to Improve Prognostics,” IEEE Trans. Autom. Sci. Eng., 13(1), pp. 344–354. [CrossRef]
Esteban, J. , Starr, A. , Willetts, R. , Hannah, P. , and Bryanston-Cross, P. , 2005, “ A Review of Data Fusion Models and Architectures: Towards Engineering Guidelines,” Neural Comput. Appl., 14(4), pp. 273–281. [CrossRef]
Subrahmanya, N. , and Shin, Y. C. , 2008, “ Automated Sensor Selection and Fusion for Monitoring and Diagnostics of Plunge Grinding,” ASME J. Manuf. Sci. Eng., 130(3), p. 031014. [CrossRef]
Gunatilaka, A. H. , and Baertlein, B. A. , 2001, “ Feature-Level and Decision-Level Fusion of Noncoincidently Sampled Sensors for Land Mine Detection,” IEEE Trans. Pattern Anal. Mach. Intell., 23(6), pp. 577–589. [CrossRef]
Malhi, A. , and Gao, R. , 2004, “ PCA-Based Feature Selection Scheme for Machine Defect Classification,” IEEE Trans. Instrum. Meas., 53(6), pp. 1517–1525. [CrossRef]
Yu, S. , Tranchevent, L. , Liu, X. , Glanzel, W. , Suykens, J. A. , De Moor, B. , and Moreau, Y. , 2012, “ Optimized Data Fusion for Kernel K-Means Clustering,” IEEE Trans. Pattern Anal. Mach. Intell., 34(5), pp. 1031–1039. [CrossRef] [PubMed]
Jin, R. , and Deng, X. , 2015, “ Ensemble Modeling for Data Fusion in Manufacturing Process Scale-Up,” IIE Trans., 47(3), pp. 203–214. [CrossRef]
Zou, J. , Arinez, J. , Chang, Q. , and Lei, Y. , 2016, “ Opportunity Window for Energy Saving and Maintenance in Stochastic Production Systems,” ASME J. Manuf. Sci. Eng., 138(12), p. 121009. [CrossRef]
Braglia, M. , and Castellano, D. , 2015, “ Improving Tool-Life Stochastic Control Through a Tool-Life Model Based on Diffusion Theory,” ASME J. Manuf. Sci. Eng., 137(4), p. 041005. [CrossRef]
Ghosh, N. , Ravi, Y. B. , Patra, A. , Mukhopadhyay, S. , Paul, S. , Mohanty, A. R. , and Chattopadhyay, A. B. , 2007, “ Estimation of Tool Wear During CNC Milling Using Neural Network Based Sensor Fusion,” Mech. Syst. Signal Process., 21(1), pp. 466–479. [CrossRef]
Wang, J. , Qiao, F. , Zhao, F. , and Sutherland, J. W. , 2016, “ A Data-Driven Model for Energy Consumption in the Sintering Process,” ASME J. Manuf. Sci. Eng., 138(10), p. 101001. [CrossRef]
Weckenmann, A. , Jiang, X. , Sommer, K. D. , Neuschaefer-Rube, U. , Seewig, J. , Shaw, L. , and Estler, T. , 2009, “ Multisensor Data Fusion in Dimensional Metrology,” CIRP Ann., 58(2), pp. 701–721. [CrossRef]
Jolliffe, I. T. , 1982, “ A Note on the Use of Principal Components in Regression,” Appl. Stat., 31(3), pp. 300–303. [CrossRef]
Zoller, P. , and Walsh, D. J. , 1995, Standard Pressure-Volume-Temperature Data for Polymers, CRC Press, Boca Raton, FL.
Kazmer, D. , 2007, Injection Mold Design Engineering, Carl Hanser Verlag, Munich, Germany. [CrossRef]
Wang, G. , and Ying, S. , 2015, “ Quality Related Fault Detection Approach Based on Orthogonal Signal Correction and Modified PLS,” IEEE Trans. Ind. Inf., 11(2), pp. 398–405.
Gao, R. X. , and Kazmer, D. , 2012, “ Multivariate Sensing and Wireless Data Communication for Process Monitoring in RF-Shielded Environment,” CIRP Ann., 61(1), pp. 523–526. [CrossRef]
Gao, R. X. , Tang, X. , Gordon, G. , and Kazmer, D. , 2014, “ Online Product Quality Monitoring Through In-Process Measurement,” CIRP Ann., 63(1), pp. 493–496. [CrossRef]
Kazmer, D. , Gordon, G. , Mendible, G. , Johnston, S. , Tang, X. , Fan, Z. , and Gao, R. X. , 2015, “ A Multivariate Sensor for Intelligent Polymer Processing,” IEEE/ASME Trans. Mechatronics, 20(3), pp. 1015–1023. [CrossRef]
Kazmer, D. , Westerdale, S. , and Hazen, D. , 2008, “ A Comparison of Staticstical Process Control (SPC) and On-Line Multivariate Analyses (MVA) for Injection Modling,” Int. Polym. Process., 23(5), pp. 447–458. [CrossRef]
Bushko, W. C. , and Stokes, V. K. , 1995, “ Solidification of Thermoviscoelastic Melts—Part I: Formulation of Model Problem,” Polym. Eng. Sci., 35(4), pp. 351–364. [CrossRef]
Pantani, R. , Speranza, V. , and Titomanlio, G. , 2016, “ Thirty Years of Modeling of Injection Molding: A Brief Review of the Contribution of UNISA Code to the Filed,” Int. Polym. Process., 31(5), pp. 655–663. [CrossRef]
Young, I. T. , Walker, J. E. , and Bowie, J. E. , 1974, “ An Analysis Technique for Biological Shape,” Inf. Control, 25(4), pp. 357–370. [CrossRef]
Kim, H. , and Kim, J. , 2000, “ Region-Based Shape Descriptor Invariantto Rotation, Scale and Translation,” Signal Process. Image Commun., 16(1), pp. 87–93. [CrossRef]
Zhang, D. , and Lu, G. , 2004, “ Review of Shape Representation and Description Techniques,” Pattern Recognit., 37(1), pp. 1–19. [CrossRef]


Grahic Jump Location
Fig. 1

Scheme for orthogonal analysis in data fusion

Grahic Jump Location
Fig. 2

Hysteresis loop in pressure–volume–temperature diagram driving changes in molded part dimensions

Grahic Jump Location
Fig. 3

Principal component decomposition in (a) PCA and (b) KPLS

Grahic Jump Location
Fig. 4

Process measurements using commercial sensors (CS) for pressure (P) and temperature (T) and developed multivariate sensor (MVS) for multiphysics measurement within an injection mold cavity

Grahic Jump Location
Fig. 5

Shrinkages predicted using analysis based on specific volume hysteresis of Eqs. (1)(7)

Grahic Jump Location
Fig. 6

Commercial sensors: modeling accuracy and contribution of each sensor

Grahic Jump Location
Fig. 7

Commercial sensors: distributions of projected sensor data with respect to part quality

Grahic Jump Location
Fig. 8

Multivariate sensor: modeling accuracy and contribution from each measured parameter

Grahic Jump Location
Fig. 9

Distribution of projected MVS sensor data points versus part quality



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In