0
Research Papers

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

[+] Author and Article Information
Chenhui Shao

Department of Mechanical Science
and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: chshao@illinois.edu

Jionghua (Judy) Jin

Department of Industrial and
Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jhjin@umich.edu

S. Jack Hu

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: jackhu@umich.edu

1Corresponding author.

Manuscript received December 27, 2016; final manuscript received March 16, 2017; published online August 24, 2017. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 139(10), 101002 (Aug 24, 2017) (11 pages) Paper No: MANU-16-1678; doi: 10.1115/1.4036347 History: Received December 27, 2016; Revised March 16, 2017

Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, it is generally expensive to acquire high-resolution spatiotemporal data in manufacturing due to the high cost of the three-dimensional (3D) measurement system or the time-consuming measurement process. In this paper, we develop a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to predictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm (GA) is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the surface progression rate. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.

FIGURES IN THIS ARTICLE
<>
Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Shao, C. , Paynabar, K. , Kim, T. H. , Jin, J. J. , Hu, S. J. , Spicer, J. P. , Wang, H. , and Abell, J. A. , 2013, “ Feature Selection for Manufacturing Process Monitoring Using Cross-Validation,” J. Manuf. Syst., 32(4), pp. 550–555. [CrossRef]
Shao, C. , Guo, W. , Kim, T. H. , Jin, J. J. , Hu, S. J. , Spicer, J. P. , and Abell, J. A. , 2014, “ Characterization and Monitoring of Tool Wear in Ultrasonic Metal Welding,” Ninth International Workshop on Microfactories (IWMF), Honolulu, HI, Oct. 5–8, pp. 161–169. http://conf.papercept.net/images/temp/IWMF/media/files/0050.pdf
Shao, C. , Kim, T. H. , Hu, S. J. , Jin, J. J. , Abell, J. A. , and Spicer, J. P. , 2016, “ Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries,” ASME J. Manuf. Sci. Eng., 138(5), p. 051005. [CrossRef]
Lee, S. S. , Shao, C. , Kim, T. H. , Hu, S. J. , Kannatey-Asibu, E. , Cai, W. W. , Spicer, J. P. , and Abell, J. A. , 2014, “ Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes,” ASME J. Manuf. Sci. Eng., 136(5), p. 051019. [CrossRef]
Zerehsaz, Y. , Shao, C. , and Jin, J. , “ Tool Wear Monitoring in Ultrasonic Welding Using High-Order Decomposition,” J. Intell. Manuf., epub.
Shao, C. , 2016, “ Data-Based Spatial and Temporal Modeling for Surface Variation Monitoring in Manufacturing,” Ph.D. thesis, University of Michigan, Ann Arbor, MI. https://deepblue.lib.umich.edu/handle/2027.42/120743
Suriano, S. , Wang, H. , Shao, C. , Hu, S. J. , and Sekhar, P. , 2015, “ Progressive Measurement and Monitoring for Multi-Resolution Data in Surface Manufacturing Considering Spatial and Cross Correlations,” IIE Trans., 47(10), pp. 1033–1052. [CrossRef]
Shao, C. , Ren, J. , Wang, H. , Jin, J. J. , and Hu, S. J. , 2017, “ Improving Machined Surface Shape Prediction by Integrating Multi-Task Learning With Cutting Force Variation Modeling,” ASME J. Manuf. Sci. Eng., 139(1), p. 011014. [CrossRef]
Yang, T.-H. , and Jackman, J. , 2000, “ Form Error Estimation Using Spatial Statistics,” ASME J. Manuf. Sci. Eng., 122(1), pp. 262–272. [CrossRef]
Zhao, H. , Jin, R. , Wu, S. , and Shi, J. , 2011, “ PDE-Constrained Gaussian Process Model on Material Removal Rate of Wire Saw Slicing Process,” ASME J. Manuf. Sci. Eng., 133(2), p. 021012. [CrossRef]
Cressie, N. , 2015, Statistics for Spatial Data, Wiley, New York.
Du, S. , and Fei, L. , 2016, “ Co-Kriging Method for Form Error Estimation Incorporating Condition Variable Measurements,” ASME J. Manuf. Sci. Eng., 138(4), p. 041003. [CrossRef]
McBratney, A. , Webster, R. , and Burgess, T. , 1981, “ The Design of Optimal Sampling Schemes for Local Estimation and Mapping of Regionalized Variables—I: Theory and Method,” Comput. Geosci., 7(4), pp. 331–334. [CrossRef]
McBratney, A. , and Webster, R. , 1981, “ The Design of Optimal Sampling Schemes for Local Estimation and Mapping of Regionalized Variables—II: Program and Examples,” Comput. Geosci., 7(4), pp. 335–365. [CrossRef]
Brus, D. , and De Gruijter, J. , 1997, “ Random Sampling or Geostatistical Modelling? Choosing Between Design-Based and Model-Based Sampling Strategies for Soil (With Discussion),” Geoderma, 80(1), pp. 1–44. [CrossRef]
Anderson, A. , Wang, G. , and Gertner, G. , 2006, “ Local Variability Based Sampling for Mapping a Soil Erosion Cover Factor by Co-Simulation With Landsat TM Images,” Int. J. Rem. Sens., 27(12), pp. 2423–2447. [CrossRef]
Wang, J.-F. , Stein, A. , Gao, B.-B. , and Ge, Y. , 2012, “ A Review of Spatial Sampling,” Spat. Stat., 2, pp. 1–14. [CrossRef]
Hoban, S. , and Strand, A. , 2015, “ Ex Situ Seed Collections Will Benefit From Considering Spatial Sampling Design and Species' Reproductive Biology,” Biol. Conserv., 187, pp. 182–191. [CrossRef]
Hinckley, E.-L. S. , Bonan, G. B. , Bowen, G. J. , Colman, B. P. , Duffy, P. A. , Goodale, C. L. , Houlton, B. Z. , Marín-Spiotta, E. , Ogle, K. , Ollinger, S. V. , Paul, E. A. , Vitousek, P. M. , Weathers, K. C. , and Williams, D. G. , 2016, “ The Soil and Plant Biogeochemistry Sampling Design for the National Ecological Observatory Network,” Ecosphere, 7(3), p. e01234. [CrossRef]
Hanks, E. M. , Hooten, M. B. , Knick, S. T. , Oyler-McCance, S. J. , Fike, J. A. , Cross, T. B. , and Schwartz, M. K. , 2016, “ Latent Spatial Models and Sampling Design for Landscape Genetics,” Ann. Appl. Stat., 10(2), pp. 1041–1062. [CrossRef]
Jin, R. , Chang, C.-J. , and Shi, J. , 2012, “ Sequential Measurement Strategy for Wafer Geometric Profile Estimation,” IIE Trans., 44(1), pp. 1–12. [CrossRef]
Zhu, Z. , and Stein, M. L. , 2006, “ Spatial Sampling Design for Prediction With Estimated Parameters,” J. Agric., Biol., Environ. Stat., 11(1), pp. 24–44. [CrossRef]
Fanshawe, T. R. , and Diggle, P. J. , 2013, “ Adaptive Sampling Design for Spatio-Temporal Prediction,” Spatio-Temporal Design: Advances in Efficient Data Acquisition, Wiley, New York, pp. 249–268. [CrossRef]
Wikle, C. K. , and Royle, J. A. , 1999, “ Space–Time Dynamic Design of Environmental Monitoring Networks,” J. Agric., Biol., Environ. Stat., 4(4), pp. 489–507. [CrossRef]
Hooten, M. B. , Wikle, C. K. , Sheriff, S. L. , and Rushin, J. W. , 2009, “ Optimal Spatio-Temporal Hybrid Sampling Designs for Ecological Monitoring,” J. Veg. Sci., 20(4), pp. 639–649. [CrossRef]
Wikle, C. K. , and Royle, J. A. , 2005, “ Dynamic Design of Ecological Monitoring Networks for Non-Gaussian Spatio-Temporal Data,” Environmetrics, 16(5), pp. 507–522. [CrossRef]
Huang, H.-C. , and Cressie, N. , 1996, “ Spatio-Temporal Prediction of Snow Water Equivalent Using the Kalman Filter,” Comput. Stat. Data Anal., 22(2), pp. 159–175. [CrossRef]
Wikle, C. K. , Berliner, L. M. , and Cressie, N. , 1998, “ Hierarchical Bayesian Space-Time Models,” Environ. Ecol. Stat., 5(2), pp. 117–154. [CrossRef]
Harrison, J. , and West, M. , 1999, Bayesian Forecasting & Dynamic Models, Springer, New York.
Hamilton, J. D. , 1994, Time Series Analysis, Vol. 2, Princeton University Press, Princeton, NJ.
Burer, S. , and Letchford, A. N. , 2012, “ Non-Convex Mixed-Integer Nonlinear Programming: A Survey,” Surv. Oper. Res. Manage. Sci., 17(2), pp. 97–106.
Hendy, M. D. , and Penny, D. , 1982, “ Branch and Bound Algorithms to Determine Minimal Evolutionary Trees,” Math. Biosci., 59(2), pp. 277–290. [CrossRef]
Ryoo, H. S. , and Sahinidis, N. V. , 1996, “ A Branch-and-Reduce Approach to Global Optimization,” J. Global Optim., 8(2), pp. 107–138. [CrossRef]
Exler, O. , Antelo, L. T. , Egea, J. A. , Alonso, A. A. , and Banga, J. R. , 2008, “ A Tabu Search-Based Algorithm for Mixed-Integer Nonlinear Problems and Its Application to Integrated Process and Control System Design,” Comput. Chem. Eng., 32(8), pp. 1877–1891. [CrossRef]
Luo, Y. , Yuan, X. , and Liu, Y. , 2007, “ An Improved PSO Algorithm for Solving Non-Convex NLP/MINLP Problems With Equality Constraints,” Comput. Chem. Eng., 31(3), pp. 153–162. [CrossRef]
Schlüter, M. , Egea, J. A. , and Banga, J. R. , 2009, “ Extended Ant Colony Optimization for Non-Convex Mixed Integer Nonlinear Programming,” Comput. Oper. Res., 36(7), pp. 2217–2229. [CrossRef]
Pillonetto, G. , Dinuzzo, F. , Chen, T. , De Nicolao, G. , and Ljung, L. , 2014, “ Kernel Methods in System Identification, Machine Learning and Function Estimation: A Survey,” Automatica, 50(3), pp. 657–682. [CrossRef]
Gombay, E. , and Serban, D. , 2009, “ Monitoring Parameter Change in Time Series Models,” J. Multivar. Anal., 100(4), pp. 715–725. [CrossRef]
Lee, S. S. , Kim, T. H. , Hu, S. J. , Cai, W. W. , and Abell, J. A. , 2015, “ Analysis of Weld Formation in Multilayer Ultrasonic Metal Welding Using High-Speed Images,” ASME J. Manuf. Sci. Eng., 137(3), p. 031016. [CrossRef]
Guo, W. , Shao, C. , Kim, T. H. , Hu, S. J. , Jin, J. J. , Spicer, J. P. , and Wang, H. , 2016, “ Online Process Monitoring With Near-Zero Misdetection for Ultrasonic Welding of Lithium-Ion Batteries: An Integration of Univariate and Multivariate Methods,” J. Manuf. Syst., 38, pp. 141–150. [CrossRef]
Cai, W. W. , Kang, B. , and Hu, S. J. , 2017, Ultrasonic Welding of Lithium-Ion Batteries, ASME, New York. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Tool surface evolution in ultrasonic metal welding [3]

Grahic Jump Location
Fig. 2

Illustration of grid segmentation for level 2 measurement

Grahic Jump Location
Fig. 3

Flowchart for the estimation and monitoring of ϕt

Grahic Jump Location
Fig. 4

Heat map of the weight matrix in the simulation study

Grahic Jump Location
Fig. 5

Performance comparison of candidate sampling methods in the simulation study: (a) measurement cost, (b) prediction precision, (c) loss, and (d) prediction RMSE

Grahic Jump Location
Fig. 6

Heat map of level 2 measurement distribution in the simulation study: (a) method 1, (b) method 2, (c) method 3, and (d) method 4

Grahic Jump Location
Fig. 7

Heat map of the average prediction variance in the simulation study: (a) method 1, (b) method 2, (c) method 3, and (d) method 4

Grahic Jump Location
Fig. 8

Spatiotemporal progression of an anvil surface in ultrasonic metal welding

Grahic Jump Location
Fig. 9

Anvil surface: (a) contour plot and (b) weight heat map

Grahic Jump Location
Fig. 10

Precision comparison of dynamic and random sampling approaches for ultrasonic metal welding

Grahic Jump Location
Fig. 11

Heat map of level 2 measurement distribution for ultrasonic metal welding: (a) random sampling and (b) dynamic sampling

Grahic Jump Location
Fig. 12

Heat map of the average prediction variance for ultrasonic metal welding: (a) random sampling and (b) dynamic sampling

Tables

Errata

Discussions

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