Developing a statistical model based on Markov chain for fatigue life prediction of double lap composite adhesive joints

The Markov chain model is one of the statistical-probabilistic approach that can predict the failure status of samples in higher cycles by using experimental results in primitive cycles and also it can be used instantaneously to control fatigue failure of parts in working. In this paper, double lap adhesive joints are subjected to cyclic loading at three different charge levels in the form of tensile load, and the purpose of this work is investigating the fatigue failure process of adhesive joints. In this study, ratcheting changes have been introduced as a failure indicator that shows the growth trend of fatigue failure. It is observed that fatigue damage occurred after 18% growth in the initial ratcheting size. These experimental results are consistent with the data obtained from the Markov chain model. Therefore, this forecasting method can predict the remaining life of double lap adhesive joints based on strain evaluations regardless of their loading history.