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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

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Figures

Grahic Jump Location
Fig. 1

Bipartite graph of assembly model

+Bipartite graph of assembly model
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Bipartite graph of assembly model
Grahic Jump Location
Fig. 2

Basic procedure for ANN prediction

+Basic procedure for ANN prediction
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Basic procedure for ANN prediction
Grahic Jump Location
Fig. 3

Graph order growth of assembly model

+Graph order growth of assembly model
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Graph order growth of assembly model
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
Grahic Jump Location
Fig. 5

Extrapolating test set

+Extrapolating test set
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Extrapolating test set
Grahic Jump Location
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. 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
Grahic Jump Location
Fig. 9

Error distribution based on graph order

+Error distribution based on graph order
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Error distribution based on graph order
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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