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

Author:

Patel Apurva1,Andrews Patrick1,Summers Joshua D.2,Harrison Erin3,Schulte Joerg4,Laine Mears M.5

Affiliation:

1. Mechanical Engineering, Clemson University, Clemson, SC 29634-0921 e-mail:

2. Professor Mechanical Engineering, Clemson University, Clemson, SC 29634-0921 e-mail:

3. Assembly Planning, BMW Manufacturing Co., LLC, Greer, SC 29651 e-mail:

4. Liaison Office, BMW Manufacturing Co., LLC, Greer, SC 29651 e-mail:

5. Professor Automotive Engineering, Clemson University, Clemson, SC 29634 e-mail:

Abstract

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.

Funder

BMW of North America

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications,Software

Reference33 articles.

1. An Axiomatic Framework for Engineering Design;ASME J. Mech. Des.,1999

2. The House of Quality;Harv. Bus. Rev.,1988

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