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research-article

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

[+] Author and Article Information
Apurva Patel

134 EIB Mechanical Engineering Clemson, SC 29634-0921
apurvap@g.clemson.edu

Erin Harrison

1400 Highway 101 South Greer, SC 29304-4100
erin.harrison@bmwmc.com

Patrick Andrews

134 EIB Mechanical Engineering Clemson, SC 29634
pcandre@g.clemson.edu

Joerg Schulte

1400 Highway 101 South Greer, SC 29304-4100
joerg.schulte@bmw.de

Joshua Summers

Mechanical Engineering Dept Clemson, SC 29634-0921
jsummer@clemson.edu

Laine Mears

4 Research Dr. 343, CGEC Greenville, SC 29607
mears@clemson.edu

1Corresponding author.

ASME doi:10.1115/1.4037179 History: Received September 03, 2016; Revised June 02, 2017

Abstract

Artificial Neural Networks (ANNs) have been used to predict assembly time and market value from assembly models. This was done by converting the assembly models into bipartite graphs and extracting 29 graph complexity metrics which were used to train 18,900 ANN prediction models. This paper presents the use of sub-assembly models instead of the entire assembly model to predict assembly quality defects at an automotive OEM. 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, and second order graph seeding, over 70% of the 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 sub-assemblies complexity data.

Copyright (c) 2017 by ASME
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