Fatigue Life Prediction Research Articles

Fatigue life prediction considering conversion of mean stress for titanium alloy under multiaxial thermo-mechanical random loading

This paper presents a thermo-mechanical fatigue (TMF) life prediction method for titanium alloy TA15 under multiaxial random amplitude loading. Through strain-controlled TMF tests, it was found that under full-reversed cyclic loading, there was a significant presence of mean stress in the stress response. The induced mean stress led to the anomalous phenomenon where the fatigue life of thermal phase angle out-of-phase (TOP) was shorter than that of thermal phase angle in-phase (TIP). Therefore, a new damage calculation model was proposed, which can take into account the pure fatigue damage considering mean stress conversion and the cumulative environmental damage. Uniaxial TMF tests, constant amplitude multiaxial TMF tests, and random amplitude multiaxial TMF tests are conducted to verify the proposed method. The predicted results for all types of tests are in a good agreement with the experimental data.

Multi-physics information-integrated neural network for fatigue life prediction of additively manufactured Hastelloy X superalloy

ABSTRACT The inherent ‘black-box’ characteristic of neural networks renders the physical interpretability of fatigue life prediction challenging, resulting in physically inconsistent prediction results. In response to this challenge, a multi-physics information-integrated neural network framework is proposed for fatigue life prediction. Rooted in the continuous damage mechanics with embedded additive manufacturing process parameters (AM-CDM), coupled with activation functions and loss functions that amalgamate physical information, a multi-tiered and multi-physical information source network framework emerges. The framework incorporates the outcomes of the AM-CDM model as input features for domain knowledge enhancement, embeds the fatigue limit within the activation function, and establishes a loss function integrating the physical model. Through the integration of multiple physical information, the model can be restricted by different physical constraints during training, thereby improving its prediction accuracy and physical consistency. Validation of the proposed framework is conducted using laser powder bed fusion (LPBF)-fabricated Hastelloy X subjected to varying orientations, post-treatments and test temperatures. The findings demonstrate that the prediction accuracy of an integrating-physics-informed neural network (IPINN) is improved by 27.51%, and its life prediction results are capped at the fatigue limit life, yielding a better physical consistency. The IPINN framework provides a novel perspective for fatigue life prediction.

A physics-informed neural network approach for predicting fatigue life of SLM 316L stainless steel based on defect features

Defects in additively manufactured materials severely limit the performance of parts in practical applications, often exposing them to the risk of fatigue failure. In order to improve the reliability and performance of additively manufactured parts, it becomes crucial to accurately predict the fatigue life of the material. Although traditional semi-empirical formulas can assess the effect of defects on the fatigue performance of parts, they still lack detailed research and consideration of defect morphology features. Therefore, this study proposes a method based on Physics-Informed Neural Networks (PINN). This method improves the predictive capability of the model and enhances its interpretability by extracting the sensitive features of critical defects and embedding known physical knowledge or fracture mechanics methods as loss functions into the training process of the neural network. Additionally, the method effectively captures the complex relationship between defect features and fatigue life, providing a deeper understanding of the model prediction results. The results show that the PINN model considering feature-related knowledge has higher prediction accuracy and reliability, and all predicted fatigue life are narrowed within 2-factor bands, enabling more accurate prediction of fatigue life for SLM 316L stainless steel under different processing conditions.

Probabilistic fatigue life prediction of notched specimens based on modified stress field intensity method under multiaxial loading

In practical engineering components, due to the existence of non-uniform stress and strain field near the notch, it brings severe challenges to fatigue life prediction when evaluating the integrity of notched components. In this study, a probabilistic fatigue life prediction model for notched specimens was established by coupling the stress field intensity (SFI) method and Weibull distribution. Firstly, the position of the dangerous point is determined by finite element calculation, and the maximum strain energy density plane through the dangerous point is defined as the critical plane. Secondly, from the perspective of 2D features, the traditional SFI method is modified based on the stress distribution on the critical plane, and a new concept of effective stress is proposed to predict the fatigue life of notched specimens by the experimental data of smooth specimens. Finally, a new non-proportional additional hardening factor is established to characterize the influence of material properties and loading path on fatigue life. The experimental data of Q345 low alloy steel and GH4169 nickel base alloy are used to compare and analyze the proposed model. The results show that the predicted life of the proposed model is in good agreement with the experimental life.

Fatigue performance simulation of reinforced concrete beams externally strengthened with side bonded CFRP sheets

Damaged Reinforced Concrete (RC) beams are commonly strengthened by bonding Carbon Fiber Reinforced Polymer (CFRP) strips to their soffits. However, inaccessible or narrow soffits limit the use of bottom-bonded CFRP strips. The Side Bonded (SB) CFRP technique overcomes this, yet studies on SB-CFRP-reinforced beams' fatigue behavior are limited. This paper presents a Finite Element (FE) model for simulating the fatigue behavior of RC beams externally strengthened with SB-CFRP sheets. The model incorporates cyclic-dependent CFRP-concrete interface degradation. Existing experimental results are utilized to validate its accuracy. Computational analyses are undertaken to explore the effects of CFRP dimensions, load and prestress levels, and end-U-shaped wrapping on fatigue performance. A simple model is proposed to predict fatigue life considering load and prestress levels. The FE model effectively predicts fatigue performance. Parametric studies indicate that narrow CFRP strips are unable to prevent concrete failure under high loads. Fatigue failure modes include rebar ruptures and CFRP delamination. Besides, the end-U-shaped wrapping reduces interface damage, extending fatigue life. The study emphasizes the sensitivity of vibration excitation method to CFRP debonding. The proposed equation efficiently predicts the fatigue life of RC beams with externally bonded CFRP strips on their sides.

Effect of Physical Parameters on Fatigue Life of Materials and Alloys: A Critical Review

Fatigue refers to the progressive and localized structural damage that occurs when a material is subjected to repeated loading and unloading, typically at levels below its ultimate strength. Several failure mechanisms have been observed in practical scenarios, encompassing high-cycle, low-cycle, thermal, surface, corrosion, and fretting fatigue. Fatigue, connected to the failure of numerous engineered products, stands out as a prevalent cause of structural failure in service. Conducting research on the advancement and application of fatigue analysis technologies is crucial because fatigue analysis plays a critical role in determining the service life of components and mitigating the risk of failure. This study compiles data from a wide range of sources and offers a thorough summary of the state of fatigue analysis. It focuses on the effects of different parameters, including hardness, temperature, residual stresses, and hardfacing, on the fatigue life of different materials and their alloys. The fatigue life of alloys is typically high at low temperatures, but it is significantly reduced at high temperatures or under high-stress conditions. One of the main causes of lower fatigue life is residual stress. High-temperature conditions and hardfacing processes cause the development of tensile residual stresses, which in turn decreases fatigue life. But, if the hardness of the material significantly increases due to hardfacing, then the fatigue life also increases. This manuscript focuses on reviewing the research on fatigue-life prediction methods, shortcomings, and recommendations.

A unified creep and fatigue life prediction approach for 316 austenitic stainless steel using machine and deep learning

Abstract316 Austenitic stainless steel (AusSS) is extensively utilized in high‐temperature industrial applications such as boiler tubes and nuclear reactor pressure vessels. These components commonly experience failure under high‐temperature and high‐pressure conditions, attributed to either creep or fatigue. Existing classical models for creep and fatigue life prediction focus on a singular failure mode (either creep or fatigue) and consider physical features only. This study aims to develop a unified life prediction model for both creep and fatigue phenomena. It synthesizes information from 12 additional unexplored chemical and microstructural features from the National Institute of Materials Science (NIMS), Japan database, and previously published literature. Machine learning (such as decision tree, random forest, and XGBoost) and deep learning (like deep neural network) algorithms are employed in the modeling process. The trained models have been cross‐validated against unseen creep and fatigue life data, demonstrating superior prediction accuracy of 96.1% for deep neural network compared with classical models.

Research on multiaxial fatigue life prediction of lifting equipment based on symbol corrected equivalent stress spectrum

Research on multiaxial fatigue life prediction of lifting equipment based on symbol corrected equivalent stress spectrum

In-situ tensile testing of fracture and strain in a selective laser melted AlSi10Mg alloy

The melt pools, the most basic units of the components fabricated by the selective laser melting (SLM) technology, play an important role in the mechanical properties of the structures. A self-developed in-situ tensile observation platform was used to carry out the in-situ tensile test of SLMed AlSi10Mg alloy specimens under the observation of optical microscope. With a series of obtained experimental data on mechanical properties and metallurgical images, combined with the digital image correlation(DIC) technology, the melt pool of the specimen and the strain of defects were analyzed, and the deformation and fracture mechanism of the SLMed AlSi10Mg alloy specimens was obtained. The results show that the proposed method successfully obtains the deformation field evolution data of the melt pool and defects, which provides experimental and theoretical support for the further study of crack extension characteristics and fatigue life prediction of SLMed metallic material components.

A Study on the Impact of Low-Frequency Random Loading and Unloading on Silo Structures

To investigate the development of cracks in the walls of reinforced concrete silos under feeding–discharge cycle loading, their causes, and their fatigue life during dynamic loading, a study was conducted using a combination of in situ monitoring and numerical simulation analysis. The following conclusions were drawn: during the loading and unloading process of the silo, the time of occurrence of the minimum pressure points follows a 4:3 ratio; extreme points are approximately 15 min apart; the minimum pressure increases during material addition and decreases during material subtraction; and the load in the non-discharge area is 1.43 times that of the load in the discharge area. That is, at the same elevation, the load borne by the silo wall is uneven, with fluctuations and rotational effects occurring. Under such uneven load conditions, the silo wall experiences significant bending and torsional moments, causing excessive local tension and leading to cracking. Our analysis showed that the most unfavorable load condition occurs when discharge ports 5 and 7 are operating simultaneously, which causes the maximum tensile damage to the silo wall. For the first time, a fatigue life prediction model for reinforced concrete silos was proposed, and the accuracy of this prediction method was verified based on actual conditions.

An engineering applicable method for multiaxial fatigue under proportional and non-proportional loads based on the octahedral plane projection

Present multiaxial fatigue models have achieved satisfactory performance in predicting multiaxial fatigue life of different loading paths; however, many of them require critical plane calculations and additional material constants. In this study, a novel multiaxial fatigue life prediction model based on octahedral shear strain energy is proposed. In the proposed model, the maximum octahedral shear strain energy serves as the damage parameter without requiring any additional material parameters. All material constants in this model can be derived from material strength constants and uniaxial fatigue constants, greatly simplifying the life prediction process. Moreover, based on the actual physical phenomenon of non-proportional additional hardening, the multiaxial non-proportional correction factor is incorporated into the plastic stage of the life equation, aligning more closely with fundamental principles in physics. Then, by utilizing the experimental data from the literature for four materials, a comparison is made between the prediction results of the novel model and those of four commonly employed models. The findings demonstrate that the new model exhibits superior simplicity and accuracy. Subsequently, through life prediction outcomes for DZ411 material across various paths, the stability and precision of the octahedral energy model are substantiated, rendering it suitable for practical engineering applications.

Probabilistic fatigue life prediction using multi-layer perceptron with maximum entropy algorithm

In the structural fatigue fields, such as aerospace, maritime, and civil engineering, the accurate construction of the Probability-Stress-Life (P-S-N) curve is crucial for preventing unexpected structural failures and is fundamental to the engineering design process. Conventional methods usually assume that the fatigue life distribution follows a normal or weibull distribution, which overlooks the differences of life distribution at different stress levels and introduces subjective errors. To address this issue, this paper proposes a new approach based a multi-layer perceptron model with maximum entropy algorithm (MPME) for probabilistic fatigue life prediction. During the derivation of life probability distribution function in this approach, the type not require any pre-assumption, thus effectively avoiding the introduction of subjective errors. Specifically, the dispersed information of fatigue life is quantified as various life distribution statistical features such as mean, variance, skewness, and kurtosis. Next, learn the continuous functional relationship between stress and these features using a multi-layer perceptron (MLP). Finally, the target probability distribution is derived using the maximum entropy algorithm, with the predicted distribution features obtained from the MLP serving as constraints for it. In addition, to ensure the consistency between the prediction of MLP and physical laws, we encode physical knowledge into the network loss to guide the updating of network parameters. Experimental results of two different material fatigue data demonstrate that, compared to traditional deterministic model-based methods and machine learning methods, the MPME in this paper can more accurately predict the probabilistic fatigue life.

Fatigue Life Prediction of Prefatigued Welded Q235 Steel Butt-Joints Treated by High Frequency Mechanical Impact

Fatigue Life Prediction of Prefatigued Welded Q235 Steel Butt-Joints Treated by High Frequency Mechanical Impact

A deep learning approach for low-cycle fatigue life prediction under thermal–mechanical loading based on a novel neural network model

In this study, a novel neural network model is designed to predict the low-cycle fatigue (LCF) life under thermal–mechanical loading. The model is composed of three sub-networks: (1) a Convolutional Neural Network (CNN), (2) a Transformer, and (3) a Fully Connected Neural Network (FCNN). These three sub-networks function as the Backbone, Neck, and Head of the model, respectively. The model, named ConTrans (an abbreviation for Convolutional Neural Network-Transformer), utilizes binarized hysteresis images as input. The predictive capability of the ConTrans model for LCF life has been validated using experimental data from four different materials. The results demonstrate that almost all predictions fall within the 2-factor band, confirming the model’s accuracy and effectiveness.

Understanding the fatigue behaviour of Ti–6Al–4V manufactured via various additive processes

Additive Manufacturing (AM) is receiving widespread attention from both industry and academia who are looking to benefit from the numerous advantageous possibilities that AM processes have to offer, such as the potential to design and produce highly complex bespoke geometries with minimal material wastage. Yet, despite this, AM also has some drawbacks. Some of the most significant include the presence of process-induced defects and the inherent surface roughness of an AM built component, both of which can have a considerable influence on the mechanical properties of the final product. This research will investigate the role of an as-built surface on the fatigue properties of AM Ti–6Al–4V manufactured by electron beam melting (EBM), laser powder bed fusion (L-PBF) and laser metal deposition with wire (LMD-w). Fatigue results have been generated alongside advanced surface profilometry, microstructural, defect and fractographic analyses that have revealed that whilst the surface roughness in the majority of instances is the primary factor impacting the fatigue performance on AM material, it cannot be considered alone. It was found that the inherent as-built (AB) surface finish was significantly different across the various AM processes, inducing a range of effective stress concentrations and thus, a contrasting impact on the resulting fatigue performance. Results from each variant have been compared against a machined and polished equivalent, to provide a further consideration as to whether the as-built surface would be suffice from a time and economical viewpoint. Statistical analysis of the generated results also allowed for an extrapolation of predicted fatigue lives in the very high cycle regime for the alternative AM Ti–6Al–4V variants.

Fracture mechanism of Ti60 alloy at high temperature in very high cycle fatigue regime and fatigue life prediction

AbstractThis study aimed to investigate the failure mechanism of Ti60 titanium alloy in the high cycle fatigue (HCF) and very high cycle fatigue (VHCF) at 500°C. Ti60 specimens were characterized before and after the 500°C VHCF test, and the fracture surfaces were observed. The results show that there are three different fatigue failure mechanisms at 500°C: (i) equiaxed primary α phase (αp) cleavage, forming small unsmooth facets and rough fracture area (RA), and fusion of non‐adjacent facets to grow into the main crack. (ii) αp grains gather into large grain with triple junction, and large grain slip to form large fusion facet. (iii) Oxide shedding and then cracks form. A dynamic recurrent neural network model is used to predict the fatigue life of Ti60 alloy, 91% of the overall predictions were within the scatter band of 3.0, and 100% were within the scatter band of 5.0.

Influence of proportional multiaxial fatigue loading on the residual mechanical properties of glass-reinforced plastic pipes

This work is dedicated to the experimental investigation of multiaxial cyclic loading influence on the residual mechanical properties of glass-reinforced plastic pipes (GRP pipes). Static, cyclic and combined (static after the preliminary cyclic loading) tests have been carried out on tubular specimens under tension with torsion. Residual stiffness curves have been made, their approximation has been carried out, using previously developed model, damage accumulation stages’ boundaries have been calculated. A specimen’s individual fatigue life prediction method, based on the initial parts of stiffness degradation curves, has been proposed. Influence of the preliminary multiaxial cyclic loading on residual Young’s modulus and tensile strength has been studied. Patterns of deformation and fracture processes have been studied, using digital image correlation method and acoustic emission method. A significant influence of fatigue damage on the residual properties of GRP pipes has been concluded.

Accelerated fatigue bench test method for rubber vibration isolators based on load spectrum compilation

The present study is focused on the development of a novel method for compiling load blocks to accelerate the fatigue bench test of rubber vibration isolators. This approach involves three main steps: Firstly, a uniaxial tensile fatigue test is conducted on rubber specimens under a constant amplitude load. According to the fatigue test data, a rubber fatigue life prediction model is established. Secondly, the mount load spectrum is statistically analyzed, and the fatigue damage distribution of the mount is calculated using the rubber fatigue life prediction model. Lastly, following the principle of damage equivalence, the load spectrum is compiled into load blocks. The fatigue bench test of the mount is then conducted using both the load spectrum and the load block as loading data, respectively. The test results demonstrate that the fatigue failure locations of the mounts are essentially identical under both loading data types, verifying the effectiveness of the proposed load block compilation method.

Numerical investigation to the effect of wear on fretting fatigue life using CDM and fracture mechanics

Fretting fatigue is a common type of problem in the engineering fields. Due to its multiaxial characteristics, it leads to a shorter overall fatigue life compared to plain fatigue. Fretting fatigue is caused by the micro slip at the contact surface, which is always accompanied by interface wear. This paper presents a refined elastic numerical simulation method combining interfacial wear with continuum damage mechanics (CDM) and fracture mechanics to predict fatigue life. The crack initiation life is determined by damage evolution law and the initiation position is determined by the total dissipated energy. Crack propagation life and path can be predicted using stress intensity factor. The stress redistribution and geometric change of contact surface caused by wear are considered in the whole fatigue process. Results indicate that the fretting fatigue simulation method incorporating wear has higher accuracy compared with the experimental results in the literature. It is noteworthy that wear retards nucleation damage process and crack propagation. As stress amplitude decreases, fatigue life increases, and delay effect of wear becomes more obvious. Additionally, wear causes the crack initiation position to move out, but crack initiation region is still near the slipping zone and propagation path does not change.

“Notch sensitivity and heat treatment effect on the fatigue behaviour of AlSi10Mg aluminium alloy processed by additive manufacturing”

This paper studies the fatigue behaviour of smooth and notched specimens made from AlSi10Mg aluminium alloy via LPBF additive manufacturing. Three material conditions were addressed: as-built, stress relief, and hot isostatic pressing. Heat treatments were conducted at a temperature to avoid Al-Si network rupture, resulting in an increased fatigue strength of both the smooth and notched specimens. This beneficial effect of the heat treatments was attributed to the homogenization of residual stresses through the specimen depth. In addition, the introduction of notches had a detrimental effect on fatigue life. Two fatigue life prediction models were successfully applied. The first one accounts for both the initiation and propagation periods using the SWT parameter and the FITNET FFS procedure, while the second one is based on a calibrated curve relating the SWT parameter and the number of cycles to failure generated from smooth specimens. Fatigue crack propagation from subsurface defects had a primary role on fatigue life of the tested alloy.