Abstract
The scope of this study is to create a model that predicts failure loads for mechanically fastened composite plates using a fuzzy expert system. The composite material used in the study was manufactured in both a fibre reinforced manner and with glass fibres. The results of a previous experimental study for cross-ply laminated composite plates that were mechanically fastened with two serial pins or bolts were used to model and predict of failure loads. Furthermore, experimental data of a preceding study were obtained with different geometrical parameters for various applied preload moments (pinned/bolted) as 2, 3, 4 and 5 Nm. In this study, a fuzzy expert system and regression analysis methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, 5 geometrical parameters and 300 test data were used. According to obtained results, it was determined that the fuzzy expert system was more appropriate than the regression analysis method for modelling and prediction. Performances of the fuzzy expert system and regression analysis method were discussed in terms of error ratios and mean absolute deviations.
Highlights
Composite materials have been in existence for many centuries; no record exists as to when people first started using them
Sen et al [12] investigated 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
A model was developed for prediction of failure loads for cross-ply laminated composite plates by using fuzzy expert system and regression analysis methods
Summary
Composite materials have been in existence for many centuries; no record exists as to when people first started using them. In order to observe the effects of bolted-joint geometry and the stacking sequence of laminated plates on the bearing strength and failure mode, parametric analysis was applied, experimentally. Sen et al [12] investigated 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. Computational experiments were carried out to predict the behaviour of crossply laminated two serial mechanically fastened composites by using both a fuzzy expert system and regression analysis methods. The performance of both methods was discussed in terms of coefficients of determination, mean absolute deviations, and mean absolute percent deviations
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