The Prediction of Fatigue Life Basing Random Forest Algorithm

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Abstract The fatigue performance of test pieces is sensitive to various influence factors. If one factor changes, the fatigue life will differ greatly. For the changes of each factors, the fatigue test must be carried on, which will increase the test cost. In this paper, in order to solve this problem, basing the machine learning method, we establish the random forest regression model to conduct a material fatigue fracture life prediction research for the 7050-T7451 aluminum alloy. For the 7050-T7451 aluminum alloy standard smooth test pieces considering six detailed factors, the fatigue test is carried out at two stress levels to obtain the fatigue fracture life. Firstly, the fatigue test data are pretreated in this paper. And the fatigue test conditions of each group are different from each other, so there are six attributes of the test conditions, including load, processing technology, roughness, material direction, thickness of the parent metal and raw material position. The test data from sets 2 to 10 are selected and randomly divided into training set and verification set with a ratio of 4:1, and the first set data was reserved as the test set. Secondly, the random forest regression model is established. And then the random forest model is trained. The model is evaluated according to the R2 determination coefficient, and the R2 determination coefficient is 0.49 after adjusting the hyperparameters of the random forest model on the verification set. It is found that the true values of the tests are all within the fatigue dispersion band four times of the predicted values. Considering the fatigue dispersibility, it is a reasonable learning model. Finally, the model is verified by the first set of test data, and the accuracy of the predicted value of the first test set is 87.7% relative to the test mean value, which the predicted result is good. Processing technology, roughness, material direction, thickness of the parent metal and raw material position can form 162 experiment combinations according to these five discrete attributes. This paper involves 10 kinds of test combinations including all kinds of attribute information. For the 152 test combinations that don’t occur, a satisfying life prediction value can be obtained using random forest model by directly importing the experimental properties without conducting experiments in the future. The fatigue fracture life prediction basing on random forest regression algorithm provides a new idea for data mining to solve traditional problems.