Prediction of Bearing Strength of Two Serial Pinned/Bolted Composite Joints using Artificial Neural Networks

Sen F., Komur M. A., Sayman O.

JOURNAL OF COMPOSITE MATERIALS, vol.44, no.11, pp.1365-1377, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 44 Issue: 11
  • Publication Date: 2010
  • Doi Number: 10.1177/0021998309353344
  • Journal Name: JOURNAL OF COMPOSITE MATERIALS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1365-1377
  • Keywords: artificial neural network, bearing strength, bolted joint, laminated composites, BOLTED JOINTS, POLYMER COMPOSITES, FAILURE, PIN, CLEARANCE, FATIGUE, PLATES, VARTM
  • Dokuz Eylül University Affiliated: Yes

Abstract

The aim of this study is to investigate the improvement of an artificial neural network (ANN) method for the prediction of bearing strength of two serial pinned/bolted E-glass reinforced epoxy composite joints. The experimental data from the previous study with different geometrical parameters without torque and various applied torque were used for developing the ANN model. Comparisons of ANN results with desired values showed that there is a good agreement between input and output variables of the experimental data. Therefore, ANN was illustrated to be a valid powerful tool for the prediction of bearing strength of two serial pinned/bolted composite joints.