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Published on: March 7, 2018
Estimation of Notched Composite Plates Fatigue Life Using Residual Strength Model Calibrated by Step-Wise Tests
Paweł Romanowicz1, Aleksander Muc2
1Institute of Machine Design, The Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland. promek@mech.pk.edu.pl.
A new residual strength model predicts fatigue life in composite plates using step-wise tests. This method calibrates models efficiently, requiring fewer samples for accurate fatigue life predictions.
Area of Science:
- Materials Science
- Mechanical Engineering
- Composite Materials
Background:
- Fatigue life prediction in composite materials is crucial for structural integrity.
- Traditional methods often require extensive testing and numerous samples.
- Notched composite structures present unique challenges due to stress concentrations.
Purpose of the Study:
- To introduce and validate a novel fatigue life prediction technique for notched composite plates.
- To develop a residual strength model calibrated using step-wise fatigue tests.
- To assess the efficiency of step-wise testing in model calibration.
Main Methods:
- Development of a residual strength-based fatigue model.
- Calibration of the model using step-wise fatigue tests.
- Experimental investigation using glass/epoxy composite plates with circular and elliptical cut-outs.
- Analysis of loading parameters: testing frequency, stress ratio, maximal fatigue stress, and cut-out geometry.
Main Results:
- The step-wise fatigue test approach enables model calibration with fewer samples.
- The fatigue model effectively incorporates key loading parameters and geometry.
- Predicted fatigue life showed good agreement with experimental durability data.
- Damage growth rate and fatigue life were analyzed under various loading conditions.
Conclusions:
- The proposed residual strength model calibrated via step-wise fatigue tests is effective for predicting fatigue life in notched composite plates.
- Step-wise testing offers a more sample-efficient calibration method.
- The model's accuracy is validated by experimental results, confirming its applicability.
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