Математическая модель многоочагового усталостного повреждения заклепочных соединений

Multiple Site Damage (MSD) is one of the significant damaging factors that limit the airworthiness of aging fleet aircrafts. In case of MSD multiple fatigue cracks initiates and propagates at the rivet holes. Those cracks are relatively short in length, but with a sufficiently large number of them and an unfavorable arrangement along the rivet joint, they can join together and form a crack of a dangerous length. To prevent this type of damage it is necessary to have adequate methods for predicting the boundary state of riveted joints during MSD. A useful approach is a numerical experiment based on Monte-Carlo simulation of the MSD main random factors – the formation of initial cracks and their growth. This paper presents a probabilistic model for predicting the initial stage of MSD – destruction of at least one bridge between the adjacent holes. A level I model is considered, which describes the process of fatigue failure of specimens without rivets but with multiple holes, which are typical for riveted joints. The initiation of fatigue cracks and their growth are modeled taking into account the laws of damage development obtained experimentally on specimens with multiple cracks. So, to simulate the random initiation of cracks in time the Weibull distribution is used. The parameters of this distribution depend on the applied stress. The growth of cracks is described by the Paris' equation, taking into account the experimentally confirmed correlation between the coefficients of this equation. The model assumes that each initiated crack propagates according to a random value of the Paris' equation exponent. The distribution of such a random value corresponds to a logarithmically normal law with experimentally obtained parameters. The criterion for the possible join of opposite cracks growing from adjacent holes is the uniting of plastic deformation zones at the tips of such cracks. The results of modeling are presented in the form of multiple site damage realization field of points in the coordinates of the number of cycles before the initiation of cracks vs. the number of cycles before the destruction of the bridge between holes.

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

An artificial neural network (ANN) extracts knowledge from a training dataset and uses this acquired knowledge to forecast outputs for any new set of inputs. When the input/output relations are complex and highly non-linear, the ANN needs a relatively large training dataset (hundreds of data points) to capture these relations adequately. This paper introduces a novel assisted-ANN modeling approach that enables the development of ANNs using small datasets, while maintaining high prediction accuracy. This approach uses parameters that are obtained using the known input/output relations (partial or full relations). These so called assistance parameters are included as ANN inputs in addition to the traditional direct independent inputs. The proposed assisted approach is applied for predicting the residual strength of panels with multiple site damage (MSD) cracks. Different assistance levels (four levels) and different training dataset sizes (from 75 down to 22 data points) are investigated, and the results are compared to the traditional approach. The results show that the assisted approach helps in achieving high predictions’ accuracy (<3% average error). The relative accuracy improvement is higher (up to 46%) for ANN learning algorithms that give lower prediction accuracy. Also, the relative accuracy improvement becomes more significant (up to 38%) for smaller dataset sizes.

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations.