With the advancement of machine learning in leading technologies, it is perceived that machine learning is a new and effective alternative for the classic fatigue life prediction. This paper provides a regression tree ensemble‐based machine learning approach to predict the fatigue life of GLARE composites. In the model, mechanical, geometrical properties and fatigue loading stresses are selected as the training parameters (so‐called features), and the GLARE fatigue life is predicted as the output of the model. Experimental data of a total of 98 pieces of GLARE specimens with nine different layups are used for the training and validation of the machine learning model. Results show that the model can provide good fatigue life prediction accuracy and model stability. The most correlated, either positively or negatively, parameters to the fatigue life span are the stress developed in the aluminum layer, the maximum cyclic stress, alternating stress, and mean fatigue stress.

Abstract. A methodology to study the fatigue of a wind turbine blade in a 10KW small wind turbine is proposed in this paper. Two working conditions (namely normal fatigue operation condition and extreme wind condition) are considered based on IEC61400-2. The maximum load calculated from both cases were used as a reference to perform material sample fatigue study. Fiber-metal laminate -GLARE 3/2 with a centre 1mm notch on the external aluminium layers was modelled based on fracture mechanics approach to calculate the stress intensity factor and fatigue crack growth rate at maximum applied stress of 240Mpa. GLARE panel fabrication and tensile tests were included. The fatigue tests were performed on unnotched samples with stress range from 80Mpa to 300Mpa and plotted into S-N curve.

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