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Backpropagation Neural Network Applications for a Welding Process Control Problem

  • Adnan Aktepe
  • Süleyman Ersöz
  • Murat Lüy
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

The aim of this study is to develop predictive Artificial Neural Network (ANN) models for welding process control of a strategic product (155 mm. artillery ammunition) in armed forces’ inventories. The critical process about the production of product is the welding process. In this process, a rotating band is welded to the body of ammunition. This is a multi-input, multi-output process. In order to tackle problems in the welding process 2 different ANN models have been developed in this study. Model 1 is a Backpropagation Neural Network (BPNN) application used for classification of defective and defect-free products. Model 2 is a reverse BPNN application used for predicting input parameters given output values. In addition, with the help of models developed mean values of best values of some input parameters are found for a defect-free weld operation.

Keywords

Backpropagation neural networks welding process control artillery ammunition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Adnan Aktepe
    • 1
  • Süleyman Ersöz
    • 1
  • Murat Lüy
    • 2
  1. 1.Faculty of Engineering, Department of Industrial EngineeringKırıkkale UniversityYahşihanTurkey
  2. 2.Faculty of Engineering, Department of Electrical and Electronics EngineeringKırıkkale UniversityYahşihanTurkey

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