Machine learning and finite element analysis: An integrated approach for fatigue lifetime prediction of adhesively bonded joints

Abstract

Since fatigue investigations are expensive and time consuming, models capable of predicting lifetime by leveraging existing experimental data are desirable.Here, this task is accomplished by combining machine learning (ML) and finite element analysis (FEA). The dataset contains 365 points comprising four adhesives with four different joint types. The model is fed with four input parameters: stress ratio and stress amplitude (functions of the applied load), and stress concentration factor and multiaxiality, which are obtained from FEA. An extremely randomized trees (ERT) algorithm, capable of dealing with small and noisy datasets, is used to design the model. After calibration, the model's performance was assessed on unseen data and compared with a linear regression model. The ERT predictions yield a significantly smaller error factor (ER) of 2.13 than that of the linear model (ER = 5.89). A relevance analysis shows that at least one FEA-based parameter must be fed into the model.

Using neural networks to predict the effect of the preload location on the natural frequencies of a cantilever beam

“…Predicting a problem output for all possible combinations of inputs within a given inputs domain using finite element method (FEM) consumes time and computational power ( Silva et al., 2021 ; Alefe et al., 2020 ). To minimize the time and power consumed by FEM, Neural networks (NNs) are proposed as function approximators to predict the problem output for any input combination ( Bohn et al., 2013 ; Javadi et al., 2009 ).…”

Effect of the porosity on the fatigue strength of metals

“…It is worth noting that the great potential of the present procedure is that the defect content analysis (performed by means of a statistical method deriving from EVT) can be easily performed by using machine learning techniques. It deserves to be mentioned that, in the last years, a great effort has been made by researchers in order to extend the use of the machine learning techniques to the fatigue field 18–20 . As a matter of fact, it has been proven that fatigue problems are appropriate for the machine learning analysis, mainly due to the large amount, complexity, and uncertainty of the available data 21 …”

“…It deserves to be mentioned that, in the last years, a great effort has been made by researchers in order to extend the use of the machine learning techniques to the fatigue field. [18][19][20] As a matter of fact, it has been proven that fatigue problems are appropriate for the machine learning analysis, mainly due to the large amount, complexity, and uncertainty of the available data. 21 The present work is structured as follows.…”

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