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Journal of Computing and Information Science in Engineering | Volume 17 | Issue 3 | research-article
Research Papers

Evaluating the Use of Artificial Neural Networks and Graph Complexity to Predict Automotive Assembly Quality Defects

[+] Author and Article Information
Apurva Patel

Mechanical Engineering,
Clemson University,
Clemson, SC 29634-0921
e-mail: apurvap@g.clemson.edu

Patrick Andrews

Mechanical Engineering,
Clemson University,
Clemson, SC 29634-0921
e-mail: pcandre@g.clemson.edu

Joshua D. Summers

Professor Mechanical Engineering,
Clemson University,
Clemson, SC 29634-0921
e-mail: jsummer@clemson.edu

Erin Harrison

Assembly Planning,
BMW Manufacturing Co., LLC,
Greer, SC 29651
e-mail: Erin.Harrison@bmwmc.com

Joerg Schulte

Liaison Office,
BMW Manufacturing Co., LLC,
Greer, SC 29651
e-mail: Joerg.Schulte@bmw.de

M. Laine Mears

Professor
Automotive Engineering,
Clemson University,
Clemson, SC 29634
e-mail: mears@clemson.edu

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received September 3, 2016; final manuscript received June 2, 2017; published online July 26, 2017. Assoc. Editor: Yong Chen.

J. Comput. Inf. Sci. Eng 17(3), 031017 (Jul 26, 2017) (10 pages) Paper No: JCISE-16-2069; doi: 10.1115/1.4037179 History: Received September 03, 2016; Revised June 02, 2017
Article
References
Figures
Tables

This paper presents the use of subassembly models instead of the entire assembly model to predict assembly quality defects at an automotive original equipment manufacturer (OEM). Specifically, artificial neural networks (ANNs) were used to predict assembly time and market value from assembly models. These models were converted into bipartite graphs from which 29 graph complexity metrics were extracted to train 18,900 ANN prediction models. The size of the training set, order of the bipartite graph, selection of training set, and defect type were experimentally studied. With a training size of 28 parts, an interpolation focused training set selection with a second-order graph seeding ensured that 70% of all predictions were within 100% of the target value. The study shows that with an increase in training size and careful selection of training sets, assembly defects can be predicted reliably from subassemblies' complexity data.
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References

1
Pahl, G. , Beitz, W. , Wallace, K. , and Blessing, L. , 2007, Engineering Design: A Systematic Approach, Springer-Verlag, London.
2
Ullman, D. G. , 2010, The Mechanical Design Process, McGraw-Hill, New York.
3
Hubka, V. , and Eder, W. E. , 1988, Theory of Technical Systems, Springer, Berlin. [CrossRef]
4
Hazelrigg, G. , 1999, “ An Axiomatic Framework for Engineering Design,” ASME J. Mech. Des., 121(3), pp. 342–347. [CrossRef]
5
Hauser, J. R. , and Clausing, D. , 1988, “ The House of Quality,” Harv. Bus. Rev., 66(3), pp. 63–73. https://hbr.org/1988/05/the-house-of-quality
6
Dieter, G. E. , and Schmidt, L. C. , 2013, Engineering Design, McGraw-Hill Education, New York.
7
Buede, D. M. , 2009, The Engineering Design of Systems, 2nd ed., Wiley, Hoboken, NJ. [CrossRef]
8
Phelan, K. , Wilson, C. , Summers, J. D. , and Kurz, M. E. , 2014, “ A Case Study of Configuration Management Methods in a Major Automotive OEM,” ASME Paper No. DETC2014-34186.
9
Phelan, K. T. , Summers, J. D. , and Guarneri, P. , 2014, “ Engineering Change Management—Verification, Validation and Testing Planning Tool Development,” The Tenth International Symposium on Tools and Methods of Competitive Engineering (TMCE), Budapest, Hungary, May 19–23, Paper No. 73.
10
Eckert, C. , Clarkson, P. J. , and Zanker, W. , 2004, “ Change and Customisation in Complex Engineering Domains,” Res. Eng. Des., 15(1), pp. 1–21. [CrossRef]
11
Clarkson, P. J. , Simons, C. , and Eckert, C. , 2004, “ Predicting Change Propagation in Complex Design,” ASME J. Mech. Des., 126(5), p. 788. [CrossRef]
12
Shankar, P. , Mathieson, J. , Ramachandran, R. , Summers, J. D. , and Mocko, G. M. , 2012, “ Review of the Design Tools and Methods to Predict Change Propagation Pathways,” The Ninth International Symposium on Tools and Methods of Competitive Engineering (TMCE), Karlsruhe, Germany, May 7–11, Paper No. 39.
13
Shankar, P. , Morkos, B. W. , and Summers, J. D. , 2012, “ Reasons for Change Propagation: A Case Study in an Automotive OEM,” Res. Eng. Des., 23(4), pp. 291–303. [CrossRef]
14
Jarratt, T. , Eckert, C. M. , Caldwell, N. , and Clarkson, P. J. , 2010, “ Engineering Change: An Overview and Perspective on the Literature,” Res. Eng. Des., 22(2), pp. 103–124. [CrossRef]
15
Mathieson, J. L. , Wallace, B. A. , and Summers, J. D. , 2010, “ Assembly Time Modeling Through Connective Complexity Metrics,” International Conference on Manufacturing Automation (ICMA), Hong Kong, China, Dec. 13–15, pp. 16–23.
16
Mathieson, J. L. , Arlitt, R. , Summers, J. D. , Stone, R. , Shanthakumar, A. , Sen, C. , Arlitt, R. , Summers, J. D. , and Stone, R. , 2011, “ Complexity as a Surrogate Mapping Between Function Models and Market Value,” ASME Paper No. DETC2011-47481.
17
Mohinder, C. V. S. , Gill, A. , and Summers, J. D. , 2016, “ Using Graph Complexity Connectivity Method to Predict Information From Design Representations—A Comparative Study,” Design Computing and Cognition’16, Chicago, IL, June 27–29, pp. 667–683.
18
Sridhar, S. , Fazelpour, M. , Gill, A. , and Summers, J. D. , 2016, “ Precision Analysis of the Graph Complexity Connectivity Method: Assembly and Function Model,” Sixth CIRP Conference on Assembly Technologies and Systems (CIRP CATS), Gothenburg, Sweden, May 16–17, p. 1095.
19
Namouz, E. Z. , and Summers, J. D. , 2014, “ Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation,” ASME J. Comput. Inf. Sci. Eng., 14(2), p. 21003. [CrossRef]
20
Namouz, E. Z. , and Summers, J. D. , 2013, “ Complexity Connectivity Metrics—Predicting Assembly Times With Low Fidelity Assembly CAD Models,” Smart Product Engineering, M. Abramovici and R. Stark , eds., Springer, Berlin, pp. 777–786. [CrossRef]
21
Owensby, J. E. , Namouz, E. Z. , Shanthakumar, A. , and Summers, J. D. , 2012, “ Representation: Extracting Mate Complexity From Assembly Models to Automatically Predict Assembly Times,” ASME Paper No. DETC2012-70995.
22
Agatonovic-Kustrin, S. , and Beresford, R. , 2000, “ Basic Concepts of Artificial Neural Network (ANN) Modeling and Its Application in Pharmaceutical Research,” J. Pharm. Biomed. Anal., 22(5), pp. 717–727. [CrossRef] [PubMed]
23
Summers, J. D. , Vargas-Hernández, N. , Zhao, Z. , Shah, J. J. , and Lacroix, Z. , 2001, “ Comparative Study of Representation Structures for Modeling Function and Behavior of Mechanical Devices,” ASME Paper No. DETC01/CIE-21243. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.102.7758&rep=rep1&type=pdf
24
Mathieson, J. L. , and Summers, J. D. , 2010, “ Complexity Metrics for Directional Node-Link System Representations: Theory and Applications,” ASME Paper No. DETC2010-28561.
25
Owensby, E. , Shanthakumar, A. , Rayate, V. , Namouz, E. Z. , and Summers, J. D. , 2011, “ Evaluation and Comparison of Two Design for Assembly Methods: Subjectivity of Information,” ASME Paper No. DETC2011-47530.
26
Widrow, B. , and Hoff, M. , 1960, “ Adaptive Switching Circuits,” Int. J. Commun. Network. Syst. Sci., 5(9A), pp. 96–104.
27
Rumelhart, D. E. , Hinton, G. E. , and Williams, R. J. , 2013, “ Learning Internal Representations by Error Propagation,” Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, D. E. Rumelhart and J. L. McClelland, eds., MIT Press, Cambridge, MA, pp. 318–362. [CrossRef]
28
Miller, M. G. , Summers, J. D. , Mathieson, J. L. , and Mocko, G. M. , 2014, “ Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks,” ASME J. Comput. Inf. Sci. Eng., 14(1), p. 11005. [CrossRef]
29
Namouz, E. Z. , and Summers, J. D. , 2013, “ Complexity Connectivity Metrics-Predicting Assembly Times With Abstract Assembly Models,” Smart Product Engineering, M. Abramovici and R. Stark , eds., Springer, Berlin, pp. 777–786.
30
Owensby, J. E. , and Summers, J. D. , 2014, “ Assembly Time Estimation: Assembly Mate Based Structural Complexity Metric Predictive Modeling,” ASME J. Comput. Inf. Sci. Eng., 14(1), p. 11004. [CrossRef]
31
Mathieson, J. L. , Wallace, B. A. , and Summers, J. D. , 2013, “ Assembly Time Modelling Through Connective Complexity Metrics,” Int. J. Comput. Integr. Manuf., 26(10), pp. 955–967.
32
Phelan, K. , Summers, J. D. , Pearce, B. , and Kurz, M. E. , 2015, “ Higher Order Interactions: Product and Configuration Study on DSM Saturation,” International Conference on Engineering Design (ICED), Milan, Italy, July 27–30, pp. 1–10. https://www.designsociety.org/publication/37878/higher_order_interactions_product_and_configuration_study_on_dsm_saturation
33
Kohavi, R. , 1995, “ A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” 14th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, QC, Canada, Aug. 20–25, Vol. 2, pp. 1137–1143. http://dl.acm.org/citation.cfm?id=1643031.1643047

Figures

Grahic Jump Location
Fig. 4

Algorithm used for generating bipartite graphs for individual parts

+Algorithm used for generating bipartite graphs for individual parts
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Algorithm used for generating bipartite graphs for individual parts
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Fig. 2

Basic procedure for ANN prediction

+Basic procedure for ANN prediction
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Basic procedure for ANN prediction
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Fig. 1

Bipartite graph of assembly model

+Bipartite graph of assembly model
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Bipartite graph of assembly model
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Fig. 8

Error distribution with respect to training size

+Error distribution with respect to training size
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Error distribution with respect to training size
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Fig. 3

Graph order growth of assembly model

+Graph order growth of assembly model
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Graph order growth of assembly model
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Fig. 9

Error distribution based on graph order

+Error distribution based on graph order
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Error distribution based on graph order
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Fig. 5

Extrapolating test set

+Extrapolating test set
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Extrapolating test set
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Fig. 6

Interpolating test set

+Interpolating test set
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Interpolating test set
Grahic Jump Location
Fig. 7

Building and testing the ANN structure

+Building and testing the ANN structure
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Building and testing the ANN structure
Grahic Jump Location
Fig. 10

Error distribution based on defect type

+Error distribution based on defect type
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Error distribution based on defect type

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