Material Fatigue Research Articles

A Review of Biomechanical Studies of Heart Valve Flutter

This paper reviews recent biomechanical studies on heart valve flutter. The function of the heart valves is essential for maintaining effective blood circulation. Heart valve flutter is a kind of small vibration phenomenon like a flag fluttering in the wind, which is related to many factors such as a thrombus, valve calcification, regurgitation, and hemolysis and material fatigue. This vibration phenomenon is particularly prevalent in valve replacement patients. The biomechanical implications of flutter are profound and can lead to micro-trauma of valve tissue, accelerating its degeneration process and increasing the risk of thrombosis. We conducted a systematic review along with a critical appraisal of published studies on heart valve flutter. In this review, we summarize and analyze the existing literature; discuss the detection methods of frequency and amplitude of heart valve flutter, and its potential effects on valve function, such as thrombosis and valve degeneration; and discuss some possible ways to avoid flutter. These findings are important for optimizing valve design, diagnosing diseases, and developing treatment strategies.

Multi-stage precipitation modeling for AA 7050 hole repairs in additive friction stir deposition

A multi-stage precipitation model is formulated to predict the microstructural evolution and explain the high performance of additive friction stir deposited aluminum alloy 7050 (AA 7050) for hole repair. The first stage is the heating process due to the high-temperature thermomechanical process of the stir. In this process, small η precipitates dissolve as they lose their stability with increasing temperature, and this causes the volume fraction of η precipitates to decrease and the concentration of Mg and Zn in the matrix to increase. The second stage is the cooling process at the end of the repair where material feeding ends and the tool is lifted away. Heterogeneous nucleation of η precipitates may occur and as the temperature cools below 250 °C, Guinier–Preston (GP) zones start to form. The final stage is the natural aging process, where the η′ precipitate starts to grow. The volume fraction and precipitate radius are predicted for each type of precipitate. Furthermore, the fine η′ precipitates and GP zones with a decent volume fraction improve the material strength and fatigue life.

Enhanced Experimental Setup and Methodology for the Investigation of Corrosion Fatigue in Metallic Biodegradable Implant Materials

Biodegradable implants as bone fixations may present a safe alternative to traditional permanent implants, reducing the risk of infections, promoting bone healing, and eliminating the need for removal surgeries. Structural integrity is an important consideration when choosing an implant material. As a biodegradable implant is being resorbed, until the natural bone has regrown, the implant material needs to provide mechanical stability. However, the corrosive environment of the human body may affect the fatigue life of the material. Conversely, mechanical stress can have an effect on electrochemical corrosion processes. This is known as corrosion fatigue. In the presented work, an experimental setup and methodology was established to analyze the corrosion fatigue of experimental bioresorbable materials while simultaneously monitoring the electrochemical processes. A double-walled measurement cell was constructed for a three-point bending test in Dulbecco‘s Phosphate-Buffered Saline (DPBS− −), which was used as simulated body fluid (SBF), at 37 ± 1 °C. The setup was combined with a three-electrode setup for corrosion measurements. Rod-shaped zinc samples were used to validate the setup’s functionality. Preliminary static and dynamic bending tests were carried out as per the outlined methodology to determine the test parameters. Open-circuit as well as potentiostatic polarization measurements were performed with and without mechanical loading. For the control, fatigue tests were performed in an air environment. The tested zinc samples were inspected via scanning electron microscopy (SEM). Based on the measured mechanical and electrochemical values as well as the SEM images, the effects of the different environments were investigated, and the setup’s functionality was verified. An analysis of the data showed that a comprehensive investigation of corrosion fatigue characteristics is feasible with the outlined approach. Therefore, this novel methodology shows great potential for furthering our understanding of the effects of corrosion on the fatigue of biodegradable implant materials.

Research on mechanical property degradation model of equipment compartment floor under atmospheric corrosion condition

A mechanical property degradation model of material fatigue life varying with stress level and corrosion time was developed in this study. An acidified NaCl solution was used to accelerate the corrosion of stainless-steel samples, and the relationship between laboratory-accelerated corrosion and atmospheric corrosion was established using corrosion thickness loss parameters. Based on the results of a pre-corroded fatigue test, lgN–S–T surface models and a modified version of Miner’s rule for calculating nonlinear mechanical damage accumulation were proposed. Taking a real part of a high-speed train as an example, critical operating mileages considering material fatigue performance degradation under corrosion conditions were calculated, which were consistent with failure mileages of the census results.

Study on the improvement of fatigue and crack resistance of semi-flexible pavement materials using the mixing -molding method

This study focuses on two significant issues pertaining to conventional grout-molded semi-flexible pavement material (P-SFP): the difficult of achieving a high grouting rate and the limited scope for enhancing asphalt content, both of which contribute to the poor crack resistance of the SFP. To address these problems, the Mixed-Molding Method Semi-Flexible Pavement Material (M-SFP) is proposed, which utilizes a combination of mixed-molding method to enhance the crack resistance and fatigue life of SFP. In order to investigate the effects of asphalt film thickness and gradation type, four different gradations and five different asphalt contents of M-SFP were designed based on the volume filling theory. The effects of asphalt film thickness and gradation type on the cracking resistance and fatigue life of M-SFP were investigated through various tests, including low-temperature bending, semi-circular bending tensile, and four-point bending fatigue test. The results were then compared with those of the control P-SFP. The test results show that the strain energy density of M-SFP is increased by 24.9 % and the fracture energy is increased by 52.1 % compared with the conventional P-SFP; the number of fatigue life times under 200με, 225με, and 250με microstrain are increased by 53.6 %, 41.3 %, and 47.3 %, respectively. Based on crack resistance test results, it is recommended that the design asphalt film thickness of M-SFP exceed 10μm. Furthermore, measurements of the air void content reveal that the M-SFP exhibits significantly better compactness compared to the P-SFP. In conclusion, it shows that the Mixing-Molding method can be used to obtain semiflexible pavement materials with higher asphalt dosage, better cracking resistance, better degree of compactness, and longer fatigue life. In addition, the correlation analysis between the cracking resistance results and the rigid-flex ratio indicates that the recommended design rigid-flex ratio for SFP should be less than 2.06.

Effect of granulated blast furnace slag on uniaxial compression fatigue properties of engineered geopolymer composites

Engineered geopolymer composites (EGCs), emerging as a novel eco-friendly cementitious material, exhibit considerable potential in diverse applications. Granulated blast furnace slag (GBFS) is a by-product of the blast furnace ironmaking process. Owing to its exceptional cementing properties, GBFS is frequently incorporated into geopolymer materials to enhance strength and stability, while also achieving slurry solidification at ambient temperature. The study examined the effect of different rates of GBFS replacement on the fatigue behavior of EGCs using uniaxial compression fatigue testing, with a particular focus on the changes in fatigue life and fatigue deformation. The results indicate that the fatigue life of EGC conforms to a Weibull distribution. While a higher GBFS replacement rate can improve the compressive strength of EGC, it has a detrimental effect on the fatigue life. Furthermore, when subjected to fatigue loading, the total strain accumulation, residual strain, and fatigue stiffness of EGC exhibited a three-stage evolutionary pattern, and remained constant during the secondary creep stage. Therefore, the comprehensive effect of adding GBFS to EGC on material strength and fatigue properties should be taken into account. This study uncovers the potential negative impacts of GBFS on the fatigue properties of EGC, contradicting the previous belief that GBFS could enhance the mechanical properties of geopolymers. Therefore, a comprehensive and meticulous assessment is necessary when contemplating the use of GBFS as a reinforcement material for EGC.

Lifing Assessment of Gas Turbine Blade Root Affected by Out-of-Tolerances.

Current and future heavy-duty gas turbines (GTs) are being developed as an alternative or support to renewable energy sources (RESs). Therefore, GTs are subjected to several instances of being switched on and off; thus, the material fatigue limit can be reached in a short time. In such a scenario, possible out-of-tolerances (OoTs) in critical components must be considered. In this paper, OoTs related to critical parameters in the attachment geometry of a rotor blade are considered to estimate their impact on component life through a 2D finite element (FE) analysis. First, a mesh refinement is performed to obtain mesh-independent results; second, OoT geometries are simulated to determine stresses and strains at the blade attachment and disc groove. The mesh refinement process is critical to ensure model accuracy for both nominal and OoT geometries. The results show that OoTs can lead to important reductions in the life of the intended component, both on the blade and disc sides. These results could be useful in updating the maintenance plan for components and could be used for future insights, with further work extending the study to 3D geometry, for example, and evaluating the effect of other geometries.

Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material

The bronze/steel diffusion welded (BSDW) bimetallic material is often applied in the rotors of piston pumps to withstand complex alternating loads under high-speed operating conditions. Although diffusion welding is a type of solid-phase welding method to achieve high-quality material connections, the fatigue problems still deserve our attention, especially the very high cycle fatigue (VHCF) and high cycle fatigue (HCF) problems. However, due to the high cost of obtaining data, it is necessary to find an efficient and high-precision fatigue life prediction method for diffusion welded materials with a small sample size. In this study, a novel method continuum damage mechanics − transfer learning method (CDM-TLM) for fatigue life prediction of BSDW material is proposed based on the transfer learning (TL) and continuum damage mechanics − finite element method (CDM-FEM). In comparison with the test results, the predicted values of BSDW material fatigue life all fall within the twice error band of the median values of the test life. The influence of frozen layers during TL and training samples in source and target models on the prediction performance is further discussed. CDM-TLM is an effective life prediction method for high-precision life prediction of BSDW material with a small sample size.

Robust optical picometrology through data diversity

Topologically structured light contains deeply subwavelength features, such as phase singularities, and the scattering of such light can therefore be sensitive to the geometry or movement of scattering objects at such scales. Indeed, it has been shown recently that single-shot optical measurements can yield positional precision better than 100 pm (less than one five-thousandth of the wavelength λ) via a deep-learning-enabled analysis of scattering patterns. Measurement performance, and the extent to which it can be sustained, are constrained by the quality and depth of neural network training data and the stability of the experimental apparatus. Here, we show that a neural network can be trained through exposure to an extended envelope of instrumental/ambient noise conditions to robustly quantify picometric displacements of a target against orders-of-magnitude larger background fluctuations, to maintain precision and accuracy of 100–150 pm in optical measurements (at λ = 488 nm) of nanowire positional change. This capability opens up a range of application opportunities, for example in the optical study of nanostructural dynamics, stiction, material fatigue, and phase transitions.

Energy based damage model for low-cycle fatigue of ductile materials

Energy based damage model for low-cycle fatigue of ductile materials

The Distributed Adaptive Bipartite Consensus Tracking Control of Networked Euler–Lagrange Systems with an Application to Quadrotor Drone Groups

Actuator faults and external disturbances, which are inevitable due to material fatigue, operational wear and tear, and unforeseen environmental impacts, cause significant threats to the control reliability and performance of networked systems. Therefore, this paper primarily focuses on the distributed adaptive bipartite consensus tracking control problem of networked Euler–Lagrange systems (ELSs) subject to actuator faults and external disturbances. A robust distributed control scheme is developed by combining the adaptive distributed observer and neural-network-based tracking controller. On the one hand, a new positive definite diagonal matrix associated with an asymmetric Laplacian matrix is constructed in the distributed observer, which can be used to estimate the leader’s information. On the other hand, neural networks are adopted to approximate the lumped uncertainties composed of unknown matrices and external disturbances in the follower model. The adaptive update laws are designed for the unknown parameters in neural networks and the actuator fault factors to ensure the boundedness of estimation errors. Finally, the proposed control scheme’s effectiveness is validated through numerical simulations using two types of typical ELS models: two-link robot manipulators and quadrotor drones. The simulation results demonstrate the robustness and reliability of the proposed control approach in the presence of actuator faults and external disturbances.

Rotating bending fatigue behavior of high-pressure diecast AlSi10MgMn alloy based on T5 heat treatment parameters

Abstract The study investigates fatigue failure, a common phenomenon in machine elements subjected to cyclic stresses. The analysis emphasizes that the actual stress experienced by materials often falls below their tensile and yield strengths due to repetitive variable stresses, leading to fatigue damage. Fatigue life is measured by the number of cycles endured before failure. This paper focuses on the aluminum alloy of AlSi10MgMn, extensively used in manufacturing due to its strength, low density, and corrosion resistance. Experimental procedures encompassed tensile testing, microstructural examination, SEM analysis, and fatigue testing. Tensile tests provided initial stress values for fatigue testing. Microstructure analyses verified that heat-treated samples exhibited precipitates. SEM analysis disclosed microstructural characteristics, while fracture surface examinations demonstrated higher fatigue resistance in heat-treated specimens. Hardness measurements were conducted, with heat-treated samples showing higher values. Theoretical calculations based on stress and cycle numbers were compared to experimental fatigue results. The derived equations aligned well with the tests. Ultimately, the study underlines the importance of heat treatment on material behavior and fatigue resistance, shedding light on alloy performance and durability enhancement.

The interpretable descriptors for fatigue performance of wrought aluminum alloys

Aluminum alloys are extensively utilized in the transportation and aerospace industries due to their excellent processability and corrosion resistance. A key aspect affecting the application of aluminum alloys is fatigue failure, with fatigue strength constituting an essential indicator for assessing the failure mode. In this study, 859 data sets were collected to evaluate the effects of aluminum alloy composition and mechanical properties on fatigue strength. To comprehensively evaluate the features that exert a great influence on the fatigue strength of wrought aluminum alloys, atomic physical quantities of aluminum alloys were added to the original data set. To improve the computational efficiency and physical interpretability of the model, Spearman correlation analysis, optimal subset method, and other methods were used to perform feature selection on the atomic feature quantities and mechanical properties in the data set. Finally, a mathematical relationship between material descriptors and fatigue strength was established based on the alloy composition and the three most relevant features in the feature screening, which helps to better understand and predict the fatigue behavior of wrought aluminum alloys.

Overhead conductor heating chamber and temperature effects on the fatigue life of aluminum alloy 6201‐T81 conductors

AbstractThis work investigates the impact of temperature on the fatigue life All Aluminum Alloy Conductors (AAAC) made of 6201‐T81. A heating chamber was developed to simulate thermal effects induced by electric current passage. Wöhler fatigue curves were generated under isothermal conditions at 75 and 150°C and later compared with the one at 20°C. The conductor exhibited similar fatigue life at 20 and 75°C; however, a significant increase of life was observed for tests at 150°C. Hardness tests and failure analysis of wire breakages were carried out on samples extracted from the conductor coil and on artificially aged samples maintained at controlled temperatures. The analyses revealed that at a temperature of 150°C, there is formation of small precipitates of magnesium and silicon within the aluminum matrix, which impeded the propagation of discontinuities, increasing the material's hardness and fatigue resistance.

A modified critical distance method for estimating fretting fatigue life of dovetail joints

AbstractIn this paper, a modified theory of critical distance (TCD) method (i.e., Gradient‐TCD method) is proposed, which combines fatigue parameter gradient and the TCD, to estimate fretting fatigue life of dovetail joints. The advantage of Gradient‐TCD method lies in utilizing the gradient parameters to effectively characterize the local effect zone caused by fretting, thereby enabling the determination of a suitable critical distance length independent of material fatigue parameters. The reliability and predictive capability of the method is validated through experimental results. Furthermore, this method demonstrates insensitivity to mesh size, resulting in an 83.6% reduction in computational time while maintaining the predictions within the two‐times scatter band. The novel Gradient‐TCD method may provide an efficient and reliable approach for fretting fatigue life prediction, which holds promise for evaluating complex full‐scale fretting problems.

Research on the Flow-Induced Vibration of Cylindrical Structures Using Lagrangian-Based Dynamic Mode Decomposition

An oscillating flow past a structure represents a complex, high-dimensional, and nonlinear flow phenomenon, which can lead to the failure of structures due to material fatigue or constraint relaxation. In order to better understand flow-induced vibration (FIV) and coupled flow fields, a numerical simulation of a two-degrees-of-freedom FIV in a cylinder was conducted. Based on the Lagrangian-based dynamic mode decomposition (L-DMD) method, the vorticity field and motion characteristics of a cylinder were decomposed, reconstructed, and predicted. A comparison was made to the traditional Eulerian-based dynamic mode decomposition (E-DMD) method. The research results show that the first-order mode in the stable phase represents the mean flow field, showcasing the slander tail vortex structure during the vortex-shedding period and the average displacement in the in-line direction. The second mode predominantly captures the crossflow displacement, with a frequency of approximately 0.43 Hz, closely matching the corresponding frequency observed in the CFD results. The higher dominant modes mainly capture outward-spreading, smaller-scale vortex structures with detail displacement characteristics. The motion of the cylinder in the in-line direction was accompanied by symmetric vortex structures, while the motion of the cylinder in the crossflow direction was associated with anti-symmetric vortex structures. Additionally, crossflow displacement will cause a symmetrical vortex structure that spreads laterally along the axis behind the cylinder. Finally, when compared with E-DMD, the L-DMD method demonstrates a notable advantage in analyzing the nonlinear characteristics of FIV.

Design and molecular dynamics of biocompatible WPU/PVA composite hydrogels with enhanced fatigue resistance: Energy dissipation of intermolecular clusters for elastomers

To engineer a hydrogel elastomer for use as an in vivo tissue replacement, it is imperative to ensure superior fatigue resistance, guaranteeing a prolonged service life. Investigating the molecular mechanisms of strain energy accumulation and transmission, which occur during the compression of elastomeric materials, is instrumental in elucidating the causes of hydrogel material fatigue. Such insights are of immense value for the development of durable artificial tissue replacements, ensuring their longevity and sustained functionality within the human body. We synthesized hydrogel elastomers through polyvinyl alcohol (PVA) and waterborne polyurethane (WPU) with good biocompatibility, and studied their fatigue behavior through molecular dynamics (MD). The results of this analysis demonstrate that the introduction of energy dissipation structures between mechanically supported molecular frameworks can enhance the relaxation efficiency of polymers. This improvement leads to enhanced resistance of hydrogels to compression fatigue. A total of 1,000,000 cycles of compression tests were conducted to verify that WPU/PVA did not exhibit any significant compression fatigue under high stress of 50 % strain. In contrast, PVA hydrogel exhibited obvious fatigue due to the absence of an energy dissipation structure. These results revealed the source of compression fatigue resistance of hydrogel elasticity and provided powerful guidance for the design and synthesis of artificial tissues.

Frequency-Dependent Fatigue Properties of Additively Manufactured PLA.

Vibration-fatigue failure occurs when a structure is dynamically excited within its natural frequency range. Unlike metals, which have constant fatigue parameters, polymers can exhibit frequency-dependent fatigue parameters, significantly affecting the vibration resilience of 3D-printed polymer structures. This manuscript presents a study utilizing a novel vibration-fatigue testing methodology to characterize the frequency dependence of polymer material fatigue parameters under constant temperature conditions. In this investigation, 3D-printed PLA samples with frequency-tunable geometry were experimentally tested on an electro-dynamical shaker with a random vibration profile. Using the validated numerical models, the estimation of vibration-fatigue life was obtained and compared to the experimental results. Performing the numerical minimization of estimated and actual fatigue lives, the frequency-dependent fatigue parameters were assessed. In particular, the results indicate that the tested samples exhibit varying fatigue parameters within the loading frequency range of 250-750 Hz. Specifically, as the loading frequency increases, the fatigue exponent increases and fatigue strength decreases. These findings confirm the frequency dependence of fatigue parameters for 3D-printed polymer structures, underscoring the necessity of experimental characterization to reliably estimate the vibration-fatigue life of 3D-printed polymer structures. The utilization of the introduced approach therefore enhances the vibration resilience of the 3D-printed polymer mechanical component.

Neural network integrated with symbolic regression for multiaxial fatigue life prediction

In the realm of structural engineering, accurately predicting multiaxial fatigue life presents a formidable challenge, stemming from the complex interplay of stress and strain across multiple directions. This research introduces the Symbolic Regression-Neural Network (SR-NN) framework, a novel integration of symbolic regression-derived expressions with neural networks aimed at enhancing predictive accuracy in this field. Initiated by selecting salient features through SHAP-informed Recursive Feature Elimination, our approach effectively minimized dimensionality while pinpointing key feature groups. Symbolic regression, newly applied to guide neural network training in the prediction of alloy material fatigue life, facilitated the generation of meaningful expressions that describe the fatigue processes. These expressions were incorporated into the network’s loss function alongside MSE loss, refining the model’s learning dynamics. Our proposed SR-NN framework demonstrated substantial improvements over traditional models in rigorous testing, notably reducing MSE by 55.4% compared to RF and 45% against SVM models, underscoring the potential of merging symbolic regression with machine learning to tackle the inherent variability and complexity of fatigue behavior across diverse materials and loading conditions. This study highlights the transformative impact of symbolic regression in improving the interpretability and accuracy of predictive models within structural engineering.

Deep reinforcement learning for tuning active vibration control on a smart piezoelectric beam

Piezoelectric transducers are used within smart structures to create functions such as energy harvesting, wave propagation or vibration control to prevent human discomfort, material fatigue, and instability. The design of the structure becomes more complex with shape optimization and the integration of multiple transducers. Most active vibration control strategies require the tuning of multiple parameters. In addition, the optimization of control methods has to consider experimental uncertainties and the global effect of local actuation. This paper presents the use of a Deep Reinforcement Learning (DRL) algorithm to tune a pseudo lead-lag controller on an experimental smart cantilever beam. The algorithm is trained to maximize a reward function that represents the objective of vibration mitigation. An experimental model is estimated from measurements to accelerate the DRL’s interaction with the environment. The paper compares DRL tuning strategies with [Formula: see text] and [Formula: see text] norm minimization approaches. It demonstrates the efficiency of DRL tuning by comparing the control performance of the different tuning methods on the model and experimental setup.