As metallic aircraft components are subject to a variety of in-service loading conditions, predicting their fatigue life has become a critical challenge. To address the failure mode mitigation of aircraft components and at the same time reduce the life cycle costs of aerospace systems, a reliable prognostics framework is essential. In this paper a hybrid prognosis model that accurately predicts the crack growth regime and the residual useful life (RUL) estimate of aluminum components is developed. The methodology integrates physics based modeling with a data-driven approach. Different types of loading conditions such as constant amplitude, random, and overload are investigated. The developed methodology is validated on an Al 2024-T351 lugjoint under fatigue loading conditions. The results indicate that fusing the measured data and physics based models improves the accuracy of prediction compared to a purely data-driven or physics based approach.

The development of a reliable structural damage prognostics framework, which can accurately predict the fatigue life of critical metallic components subjected to a variety of in-service loading conditions, is important for many engineering applications. In this article, a novel integrated structural damage localization method is developed for prediction of cracks in aluminum components. The proposed methodology combines a physics-based prognosis model with a data-driven localization approach to estimate the crack growth. Specifically, particle filtering is used to iteratively combine the predicted crack location from prognostic model with the estimated crack location from localization algorithm to probabilistically estimate the crack location at each time instant. At each time step, the crack location predicted by the prognosis model is used as a priori knowledge (dynamic prior) and combined with the likelihood function of the localization algorithm for accurate crack location estimation. For improving the robustness of the localization framework, online temperature estimation is carried out. The model is validated using experimental data obtained from fatigue tests preformed on an Al2024-T351 lug joint. The results indicate that the proposed method is capable of tracking the crack length with an error of less than 1 mm for the majority of the presented cases.

Fatigue damage characteristics of aluminium alloy under complex biaxial loads such as in‐phase and out‐of‐phase loading conditions and different biaxiality ratios have been investigated. The effects of microscale phenomena on macroscale crack growth were studied to develop an in‐depth understanding of crack nucleation and growth. Material characterization was conducted to study the microstructure variability. Scanning electron microscopy was used to identify the second phase particles, and energy dispersive X‐ray spectroscopy was performed to analyse their phases and elements. Extensive quasi‐static and fatigue tests were conducted on Al7075‐T651 cruciform specimens over a wide range of load ratios and phases. Detailed fractography analysis was conducted to understand the crack growth behaviour observed during the fatigue tests. Significant differences in crack initiation and propagation behaviour were observed when a phase difference was applied. Primarily, crack retardation and splitting were observed because of the constantly varying mode mixity caused by phase difference. The crack growth behaviour and fatigue lives under out‐of‐phase loading were compared with those under in‐phase loading to understand the effect of mixed‐mode fracture.

No abstract

A hybrid prognosis model has been developed to predict the crack propagation in aluminum alloys subject to biaxial in-phase and out-of-phase fatigue loading conditions. The novel methodology combines physics-based modeling with machine learning techniques to predict crack growth in aluminum alloys. Understanding the failure mechanisms under these complex loading conditions is critical to developing reliable prognostic models. Therefore, extensive fatigue tests were conducted to study the failure modes of carefully designed cruciform specimens. Energy release rate was used as the physics-based parameter and Gaussian process was used to model the complex nonlinear relationships in the prognosis framework. The methodology was used to predict crack propagation in Al7075-T651 under a range of loading conditions. The predictions from the prognosis model were validated using the data obtained from the biaxial tests. The results indicate that the algorithm is able to accurately predict the crack propagation under proportional, non-proportional, in-phase, and out-of-phase loading conditions.