Nonlinear Cumulative Damage Model Research Articles

A novel nonlinear fatigue cumulative damage model based on machine learning

Machine learning serves as a potent tool for predicting fatigue damage. By integrating traditional physics-based models with machine learning techniques, prediction accuracy can be notably enhanced. Drawing from a substantial pool of experimental data, this article introduces a novel nonlinear fatigue cumulative damage model. This model combines the traditional Manson-Halford model with machine learning algorithms, enabling the prediction of residual fatigue damage across various materials. The predictive capabilities of this model are compared with those of traditional approaches. The article subsequently investigates the key parameters influencing the nonlinear fatigue cumulative damage model. The findings demonstrate that the proposed model exhibits significantly higher accuracy in residual fatigue damage prediction compared to traditional methods. Notably, the primary factor impacting the nonlinear cumulative fatigue damage model is identified as the first level loading damage. This study underscores the substantial potential of the proposed nonlinear fatigue cumulative damage model for predicting residual fatigue damage.

A modified Manson-Halford model based on improved WOA for fatigue life prediction under multi-level loading

The Manson-Halford (M-H) nonlinear cumulative damage model is widely applied for fatigue life analysis problems under multi-level loading. In this model, the influence of loading sequence on the fatigue life can be better considerer, but the loading interaction effect is ignored. An improved whale optimization algorithm (IWOA) by integrating multiple strategies is proposed. The ability of global search and local exploitation is balanced and improved through nonlinear convergence factor, adaptive weighting factors and the Cauchy reverse learning strategies. In order to fully account for loading interaction effect, loading weighting factors are introduced to modify the M-H model, and the parameters are optimized through the global search properties of IWOA. The model is evaluated on multi-level loading fatigue experimental data from five metal materials and two aluminum alloy welded joints. The results suggest that the proposed IWOA has better optimization accuracy compared to the standard whale optimization algorithm (WOA). The proposed modified M-H model has better prediction performance compared to the four traditional cumulative damage models, which can be effectively applied to multi-level loading fatigue life analysis problems under actual working conditions. The proposed model is useful for the study of fatigue life evaluation methods.

A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction

AbstractAero‐engine turbine blades are subject to combined high and low cycle fatigue (CCF) loadings during operations. In this paper, a modified nonlinear cumulative damage model is developed based on linear damage accumulation theory and static toughness exhaustion to predict the CCF life of turbine blades and common blade materials. An interaction factor is created to reflect the integrated effect of high cycle fatigue and low cycle fatigue (HCF‐LCF). Available test data from TC4 alloy and other two alloy materials, together with two turbine blades are utilized to validate the robustness and accuracy of the proposed approach. Comparative results demonstrate that the proposed model holds better performance in life predictions under CCF loadings.

Prognostics and Health Management Using Nonlinear Cumulative Damage Model for Electronic Devices Under Variable Loading

Prognostics and Health Management Using Nonlinear Cumulative Damage Model for Electronic Devices Under Variable Loading

Probabilistic fatigue cumulative damage model considering the randomness of load and material properties

A probabilistic fatigue cumulative damage model that can be applied to the fatigue life prediction of aluminum alloy under spectrum loading is proposed in this paper. The model is constructed by a novel nonlinear cumulative damage model, the P–S–N curve, and the probabilistic load–exceedance curve. It introduces the randomness of the loading process and fatigue resistance of the material to the deterministic model. In this model, it is derived that the cumulative damage under fleet spectrum loading exhibits an approximate lognormal distribution, and a method to calculate the distribution parameters is provided. Finally, the validity of the proposed probabilistic model is verified through fatigue tests on 2297-T87 aluminum–lithium alloy specimens under random loading, demonstrating its capability to capture the probabilistic nature of the fatigue behavior of aluminum alloy effectively.

A nonlinear cumulative fatigue damage life prediction model under combined cycle fatigue loading considering load interaction

In the present work, combined high and low cycle fatigue (CCF) tests were performed on GH4169 nickel-based alloy under stress-controlled conditions. The interaction between high cycle fatigue (HCF) and low cycle fatigue (LCF) results in a complex process of fatigue damage evolution under CCF loading. Based on the fatigue damage curve and the theory of equivalent damage, considering the interaction between HCF and LCF loading, a nonlinear cumulative damage model is proposed under CCF loading. The proposed model is deduced from the fatigue damage curve under CCF loading, which establishes a connection between the LCF and HCF damage curve. Three materials, including the other two materials, namely TC11 and Al 2024-T3, published in the relevant literature, are used to verify the proposed model. The experimental results demonstrate that the proposed model has better prediction results than those for Miner’s rule and the Trufyakov-Kovalchuk (T-K) model. The validation of the proposed model shows its superior performance.

Cumulative fatigue damage theories for metals: review and prospects

PurposeScholars mainly propose and establish theoretical models of cumulative fatigue damage for their research fields. This review aims to select the applicable model from many fatigue damage models according to the actual situation. However, relatively few models can be generally accepted and widely used.Design/methodology/approachThis review introduces the development of cumulative damage theory. Then, several typical models are selected from linear and nonlinear cumulative damage models to perform data analyses and obtain the fatigue life for the metal.FindingsConsidering the energy law and strength degradation, the nonlinear fatigue cumulative damage model can better reflect the fatigue damage under constant and multi-stage variable amplitude loading. In the following research, the complex uncertainty of the model in the fatigue damage process can be considered, as well as the combination of advanced machine learning techniques to reduce the prediction error.Originality/valueThis review compares the advantages and disadvantages of various mainstream cumulative damage research methods. It provides a reference for further research into the theories of cumulative fatigue damage.

Modified nonlinear cumulative damage model considering residual strength degradation and gear reliability analysis

AbstractThe fatigue damage of gear is caused by the coexistence and mutual influence of residual strength degradation and cumulative damage. The residual strength decreases continuously with the change of the loads and the increase of stress cycles. Most of the traditional cumulative damage models ignore the effect of residual strength degradation on cumulative damage. The exponential parameter in the traditional Corten‐Dolan model was defined as exponentials function related to the strength degradation coefficient, and the strength degradation coefficient was deduced by analyzing the residual strength degradation model, and the modified Corten‐Dolan model considering the residual strength degradation was established. Based on the stress‐strength interference model, a gear reliability model considering residual strength degradation was established. The effectiveness of the strength degradation model and the accuracy of the modified Corten‐Dolan model are verified by the analysis of the test data, which provides a new idea for the reliability analysis of gear. The accuracy of the gear reliability model proposed in this paper was verified by Monte‐Carlo (M‐C) simulation, which has practical significance for engineering application.

Fatigue Test on Heavy Haul Railway Tunnel Bottom Structure With Base Cavity

The presence of a base cavity will degrade the bearing capacity of tunnel bottom structures, and will also have a noteworthy impact on its fatigue performance. In order to study the fatigue performance and cumulative damage to tunnel bottom structure, a series of bending fatigue tests are conducted by reference to a heavy haul railway tunnel with a base cavity. Through the tests, fatigue evolution characteristics of tunnel bottom structure with cavity are obtained, then based on the expression of S-N curves, a non-linear fatigue cumulative damage model is therefore proposed, the deflection evolution and cumulative damage evolution can be divided into three stages, and characterized with an “s-shaped” curve. The tests results reveal that the damage to the tunnel bottom structure develops rapidly when a cavity exists in the base rock, fatigue occurs more easily, and the fatigue life of specimens decreases with the increase of the stress level and cavity width.

An Improved Nonlinear Cumulative Damage Model Considering the Influence of Load Sequence and Its Experimental Verification

According to the change characteristics in the toughness of the metal material during the fatigue damage process, the fatigue tests were carried out with the standard 18CrNiMo7-6 material. Scanning the fracture with an electron microscope explains the lack of linear cumulative damage in the mechanism. According to the obtained results, a nonlinear damage accumulation model which considered the loading sequence state under the toughness dissipation model was established. The recursive formula was devised under two-level. The fatigue test data verification of three metal materials showed that using this model to predict fatigue life is satisfactory and suitable for engineering applications.

A Novel Probabilistic Fatigue Life Prediction Method for Welded Structures Based on gPC

The traditional fatigue life prediction methods based on the S-N curve all believe that the parameters in the model are deterministic constants and can be categorized to the deterministic life prediction. However, in practice, it is difficult to carry out a large number of experiments due to the limitation of time or the possible shortage of funds. In addition, the specimens used in the experiments are not exactly the same, and the test operations and data reading depend on the accuracy of the test equipment as well as the subjective judgment of the testers, which result to the uncertainty of the S-N curve. Therefore, the uncertainty should be considered in order to improve the accuracy of the fatigue life prediction. In this paper, the uncertain factors affecting the fatigue life of welded joints are summarized, and the generalized polynomial chaos (gPC) is introduced into fatigue life prediction. A novel probabilistic fatigue life prediction method combined with the nonlinear cumulative damage model considering the uncertainty of the S-N curve is constructed. An illustrative example is presented to demonstrate the advantages of the proposed approach.

Cumulative Damage and Life Prediction Models for High-Cycle Fatigue of Metals: A Review

Fatigue design of engineering structures is typically based on lifetime calculation using a cumulative damage law. The linear damage rule by Miner is the universal standard for fatigue design even though numerous experimental studies have shown its deficiencies and possible non-conservative outcomes. In an effort to overcome these deficiencies, many nonlinear cumulative damage models and life prediction models have been developed since; however, none of them have found wide acceptance. This review article aims to provide a comprehensive overview of the state-of-the art in cumulative damage and lifetime prediction models for endurance based high-cycle fatigue design of metal structures.

Prediction of combined cycle fatigue life of TC11 alloy based on modified nonlinear cumulative damage model

The nonlinear cumulative damage model is modified to have high prediction accuracy when the high-low cycle stress frequency ratio m is large (m ≥ 500). The low cycle fatigue (LCF) tests, high cycle fatigue (HCF) tests and combined high and low cycle fatigue (CCF) tests of TC11 titanium alloy were carried out, and the influencing factors of CCF life were analysed. The CCF life declines with the decrease of the ratio of high-low cycle stress frequency m. Both linear and nonlinear cumulative damage models are used to predict the CCF life. The CCF life prediction error of the linear cumulative damage model is great and the predictions tend to be overestimated, which is dangerous for engineering application. The accuracy is relatively high when the high-low cycle stress frequency ratio m ≤ 500. The accuracy of nonlinear cumulative damage model is higher than that of linear model when the high-low cycle stress frequency ratio m ≥ 500. Based on the relationship between high cycle average stress σmajor and material yield limit σp,0.2, a correction term is added to the nonlinear cumulative damage model and verified, which made the modified model more accurate when m ≥ 500.

An improved nonlinear cumulative damage model for strength degradation considering loading sequence

In order to determine the effect of different loads on fatigue damage, a strength degradation model is proposed according to the law of residual strength degradation of metal materials. The model is verified with the strength degradation test data, and the results show that the model can describe the strength degradation process of general metal materials well. Combined with the strength degradation model, an improved equivalent damage model for different loading sequences is proposed. On this basis, a nonlinear fatigue cumulative damage model based on strength degradation is derived. The cumulative damage model is applied to the estimation of fatigue residual life under two-, three-, and four-stage loads to investigate the effects of different loading sequence on fatigue damage under various loading conditions. Combining with experimental data, it is verified that the cumulative damage model can accurately estimate the fatigue life under two-, three-, and four-stage loads.

Interval dynamic reliability analysis of mechanical components under multistage load based on strength degradation

AbstractTo further study the law of strength degradation, the residual strength degradation model is established based on the definition of fatigue damage, considering the interaction of various uncertain factors and time factors in service environment. Combined with equivalent damage model, a nonlinear cumulative damage model is proposed, which takes the interaction among loading loads into account and improves the accuracy of calculation. Additionally, the equivalent transformation of multistage load is studied using interval theory. According to the interval dynamic nonprobability reliability prediction model, a dynamic reliability analysis of the interval model is carried out. Dynamic reliability of the component is analyzed under multistage load accumulation damage to verify the effectiveness of the method.

Cavity influence on fatigue performance of heavy haul railway Tunnel’s bottom structure

The smoothness of tunnel base directly affects the safety of train operation, and tunnel base diseases are usually difficult to detect and deal with due to the distinguishing features of heavy haul railway, the base diseases become one of the most harmful tunnel diseases. Therefore, with the increasing axle load and the basal deterioration of the existing heavy haul railway in China, studying the fatigue performance of the tunnel bottom structure is essential. A 3D train-tunnel-surrounding rock numerical model is established to obtain the dynamic response of the tunnel bottom structure under 30 t axle load trains. Then refer to the stress state and fatigue vulnerable position which are originated from the numerical simulation, a series bending fatigue tests are carried out to study the cavity influence on fatigue performance of heavy haul railway tunnel’s bottom structure in this paper. A non-linear cumulative damage model is proposed to quantify fatigue damage evolution characteristics of tunnel bottom structure with base cavity through the tests. The results suggest that, the base rock cavity remarkably reduces the fatigue life of the specimen and increases the development rate of damage; the existence of cavity not only deteriorates the bearing capacity of the structure, but also greatly improves the dynamic response and stress level of the bottom structure and base rock, which has multiple adverse effects on the fatigue performance of the tunnel bottom structure, and also accelerates the expansion of the cavity itself.

Rubber fatigue life prediction using a random forest method and nonlinear cumulative fatigue damage model

ABSTRACTIn this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are used as independent variables, and the prediction model of rubber fatigue life under constant amplitude stress is established. A nonlinear cumulative fatigue damage model is proposed to calculate rubber fatigue life under the variable amplitude stress. Results show that the random forest method has high precision and generalization capability for rubber fatigue life prediction under constant amplitude stress and the nonlinear cumulative fatigue damage model could be employed to calculate the fatigue life of rubber under variable amplitude stress with enough accuracy according to the constant amplitude stress fatigue life data. This research can provide a reference for rubber fatigue life prediction. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48519.

A novel non-linear cumulative fatigue damage model based on the degradation of material memory

Many fatigue damage models have been investigated based on the S– N curve or modified S– N curve; however, a number of them require additional efforts to determine the material parameters or do not consider the loading history (loading interactions, loading sequences, loading levels, etc.). These limitations can result in extreme deviations for estimating the fatigue life in real-world scenarios. To address these limitations, a new fatigue damage model is developed based on the material memory, which can be described as the degradation of mechanical properties under cyclic loadings. Comparisons with three models are used to demonstrate the validity of the proposed model. Furthermore, four sets of experimental data under two-stress and four-stress levels are carried out to verify the validation of the proposed model, which improves the residual life estimation over the three existing models used for comparison.

Novel analytical model for fatigue cumulative damage estimation under random loading

AbstractA newly nonlinear cumulative damage model associated with the S‐N curve, which preserves the compatibility conditions established for S‐N plane, was proposed and assessed by Eskandari and Kim in 2015. The model requires one exponent, in addition to the S‐N curve, to calculate the residual strength. A methodology is established to determine analytically the lower bound value of the exponent. However, this value proved to be too low for a quasi‐isotropic glass‐fibre laminate, based on published residual strength data. Further on, good predictions are obtained for ascending and descending ordered block spectrum. Yet it failed at fully random spectrum. The model was modified by imposing a very small decrease on model exponent towards the lower bound value, each time an increase in peak stress occurs. This modified model has been found to give good agreement with fatigue experimental data at different spectrum loads.

Comparative Study of Nonlinear Cumulative Creep Damage Model and Time Fraction Rule to Predict Residual Creep Life of Pressure Vessels - A Review

Comparative Study of Nonlinear Cumulative Creep Damage Model and Time Fraction Rule to Predict Residual Creep Life of Pressure Vessels - A Review