Predicting Assembly Quality of Complex Structures Using Data Mining

Ekaterina S. Ponomareva⁵,
Kesheng Wang⁵ &
Terje K. Lien⁵

Part of the book series: IFIP International Federation for Information Processing ((IFIPAICT,volume 207))

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International Conference on Programming Languages for Manufacturing

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Abstract

Our research aims at obtaining the relevant factors that cause the decrease in quality of assembly parts. Decision tree technique was employed to induce useful information hidden within a vast collection of data. The major objective of this study was to classify the existing data into certain types of segmentations and then predict the behaviour of a ball joint assembly. The intervals of the rolling time and achieved rolling force leading to occurrence of the high moment values of the ball joint during testing stage have been found.

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Authors and Affiliations

Department of Production and Quality Engieering, Norwegian University of Science and Technology, NTNU, Norway
Ekaterina S. Ponomareva, Kesheng Wang & Terje K. Lien

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Ekaterina S. Ponomareva
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Kesheng Wang
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Terje K. Lien
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Editors and Affiliations

Department of Production and Quality Engineering, Norwegian University of Science and Technology, Norway
Kesheng Wang
Computer and Automation Institute and Technical University of Budapest, Hungary
George L. Kovacs
Rensselaer Polytechnic Institute, USA
Michael Wozny
Shanghai University, Chian
Minglun Fang

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Ponomareva, E.S., Wang, K., Lien, T.K. (2006). Predicting Assembly Quality of Complex Structures Using Data Mining. In: Wang, K., Kovacs, G.L., Wozny, M., Fang, M. (eds) Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management. PROLAMAT 2006. IFIP International Federation for Information Processing, vol 207. Springer, Boston, MA . https://doi.org/10.1007/0-387-34403-9_36

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DOI: https://doi.org/10.1007/0-387-34403-9_36
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-34402-7
Online ISBN: 978-0-387-34403-4
eBook Packages: Computer ScienceComputer Science (R0)

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