•AlSi10Mg additively manufactured by 3 different processes.•Fatigue properties are controlled by the size of manufacturing defects.•Defect-based modelling allows to express a relationship between ...fatigue properties and material quality.
Ability to predict the fatigue resistance of parts produced by additive manufacturing (AM) is a very current and frequently relevant open issue. The qualification of AM structural parts often needs a costly and time-consuming series of fatigue tests, on both samples and full-scale parts. Proper control of the AM process allows obtaining comparable and even better fatigue resistance than those obtained with standard manufacturing. Despite this, the experimental results often show a large scatter, mostly due to the presence of defects. In this framework, the present work summarizes the research activity aimed at modelling the high cycle fatigue (HCF) resistance in the presence of defects, focusing on AlSi10Mg produced by selective laser melting. Three batches of samples were investigated by X-ray micro computed tomography and tested under fatigue. A lower bound resistance curve was obtained, which introduced artificial defects of size corresponding to that of the largest occurring defects.
The analysis shows that a combination of defect-tolerant design with well-established and newly proposed fracture mechanics methods is the key to expressing the relationship between the fatigue strength and material quality. This is done through suitable statistics of material defects induced by the AM process.
The same concepts are then applied in a fatigue crack growth simulation model based on the maximum defect size, for estimating both the life and scatter of the data in the region of elastic material response. Based on this wide activity, it can be concluded that fracture mechanics-based analysis appears to be the tool needed for supporting the application of additive manufacturing to safety-critical components and their qualification.
•Analytical thermo-mechanical isostrain elastoplastic model of fiber metal laminates.•Internal stress analysis of cyclically loaded hybrid fiber metal laminates.•Lamina level fatigue life prediction ...model for fiber metal laminates.•Very good convergence of modeled and experimentally determined behavior.
In this paper, the new analytical prediction models were proposed for assessment of static strength and fatigue life of Fiber Metal Laminates (FMLs). The proposed static model combines composites mechanical linear-elastic behavior with non-linear elastoplastic behavior of metals, including curing stresses in laminae. Based on determined internal stress distribution through the thickness of the laminate, the fatigue life of FML is modeled at the lamina level, based on S-N mother curves of monolithic materials. The new criterion for prediction of residual fatigue life period in FMLs was proposed. Theoretical work was experimentally validated with good convergence of results.
•A CDM-FE model investigated the effects of surface roughness on RCF.•Optical profilometer measurements established tribo-surface parameters.•Fatigue life is reduced as specific film thickness ...(λ-ratio) is decreased.•Surface failures reduce fatigue life more than subsurface failures.•The competitive RCF failure mechanism exists with the introduction of roughness.
In this study, a continuum damage mechanics (CDM) finite element (FE) model was developed to investigate the effects of surface roughness on rolling contact fatigue (RCF) life of non-conformal contacts. In order to assess the surface roughness of tribo-components, twelve deep groove rolling element bearings from various companies in different sizes were procured and measured using an optical surface profilometer. The roughness average (Ra) and the root mean square of surface roughness (RMS, σ) ranged from a low of 0.03, 0.05 µm to a high of 0.14, 0.20 µm, respectively. The number of peaks and valleys per 400 µm were measured and calculated. The number of peaks ranged from 11 to 31 (greater than99.5% Confidence Interval). The measured surfaces also revealed that a sinusoidal pattern can be used to accurately represent the surface patterns. The sinusoidal surface pattern was used to determine the elastohydrodynamic lubrication (EHL) pressure distribution between an equivalent rough surface in contact with a smooth surface. Four roughness amplitude were used to generate specific film thicknesses (λ-ratios) resulting in full to mixed EHL lubrication regimes. The EHL pressure distributions were replaced with representative symmetric Hertzian pressure distributions in order to remove the effect of asymmetry of an EHL pressure distribution. The resulting symmetric pressure distributions were used in a finite element continuum damage mechanics model to determine RCF life of machine elements operating in specific film thickness range of 1 < λ < 10. The RCF results from the FE model indicate that as roughness amplitude increases or lambda ratio decreases, the fatigue lives decrease for the various frequencies. Additionally, subsurface failure fatigue lives are reduced as roughness frequency increases regardless of amplitude or Hertzian pressure. The RCF results also indicate that for the low frequency pressure distribution the contact is most susceptible to surface failure, whereas for high frequency pressure distribution the contact resists surface failure. The results from this investigation were used to develop surface roughness effects for various RCF life equations commonly used in rolling element bearing application.
•The proposed method is proposed for remaining life prediction and uncertainty quantification under two-step loading.•The proposed method is verified by a database containing 12 metallic materials, ...328 two-step loading experimental results.•The proposed method can achieve greater accuracy and reliability in prediction of remaining life under two-step loading.•The proposed method shows effective application in the estimation of the untrained Al-2024-T42 material.
Remaining fatigue life prediction is vital for engineering structures to ensure safety and reliability. It can be more challenging when the structures suffer variable amplitude loadings because of the complex, non-uniform of the fatigue damage accumulation and inherent noise, uncertainty in the data. To further tackle the problem, the Gaussian process regression (GPR) is introduced, which can simultaneously estimate the output value and quantify the associated uncertainty. Therefore, a GPR-based remaining fatigue life prediction method is proposed to predict the remaining fatigue life for metallic materials under two-step loading in this paper. The proposed method is comprehensively evaluated on the dataset containing 12 materials, 328 samples in total. The proposed method achieves the lowest mean square error (MSE), mean absolute percentage error (MAPE), residual standard deviation (RSD) values and the highest correlation coefficient (CC) values among the six machine learning methods and the two model-driven methods. Those results indicate that the proposed method can achieve greater accuracy and reliability in remaining life prediction under two-step loading, which illustrate the effectiveness of the proposed method as a data-driven method in the field of remaining life prediction.
•A new framework is presented for data-driven fatigue life prediction of AM alloys.•Computational strategy is demonstrated for the CDM based machine learning method.•Predicted fatigue lives of AM ...titanium alloys are verified by experimental data.•Parametric studies are investigated on prediction performance and fatigue lives.
Additive manufacturing (AM) technology has been widely employed in the fabrication of titanium alloy parts for aerospace engineering applications. In this paper, a damage mechanics based machine learning framework is presented for the data-driven fatigue life prediction of AM titanium alloy. At first, a theoretical framework including the damage mechanics based fatigue models and random forest model is presented for the fatigue damage analysis and life prediction of the AM titanium alloys under cyclic loadings. Second, a computational methodology is demonstrated in detail from two aspects, that is, the numerical implementation of the damage mechanics based fatigue models and the construction process of the random forest model. After that, fatigue life predictions are carried out for the AM titanium alloy smooth and notched specimens under different stress levels and stress ratios. The predicted results are compared with the experimental data to verify the proposed method. Finally, parametric studies are investigated on the prediction performance and fatigue lives for the AM titanium alloys.
•New multiaxial fatigue life assessment model based on two uniaxial strain-controlled tests.•Significant reduction of overall cost and effort associated with the evaluation of multiaxial fatigue ...life.•Method based on strain energy density fatigue master curves defined as the sum of positive elastic and plastic components.•Lifetime predictions nearly independent on the pairs of tests selected to generate the fatigue master curve.•Very good correlation between experimental and predicted lives with all points within a factor of 2.
The present paper focuses on the application of the total strain energy density approach to assess fatigue life in notched samples subjected to multiaxial loading. This approach requires, as a starting point, a fatigue master curve defined in terms of total strain energy density versus number of cycles to failure, which is usually determined from a set of uniaxial fatigue tests using smooth standard specimens. In order to reduce the time and cost associated with the generation of the fatigue master curve, a straightforward methodology based on the outcomes of only two uniaxial strain-controlled tests is proposed. The methodology is applied to round bars with U-shaped notches subjected to proportional bending-torsion loading histories. A very good correlation between the experimental and the predicted life of the notched specimens is observed. In addition, the theoretical predictions are nearly independent of the pairs of uniaxial strain-controlled tests selected to obtain the fatigue master curve, which indicates a good robustness of the suggested methodology.
Hot section components of aircraft engines like high pressure turbine (HPT) discs usually operate under complex loadings coupled with multi‐source uncertainties. The effect of these uncertainties on ...structural response of HPT discs should be accounted for its fatigue life and reliability assessment. In this study, a probabilistic framework for fatigue reliability analysis is established by incorporating FE simulations with Latin hypercube sampling to quantify the influence of material variability and load variations. Particularly, variability in material response is characterized by combining the Chaboche constitutive model with Fatemi‐Socie criterion. Results from fatigue reliability and sensitivity analysis of a HPT disc indicated that dispersions of basic variables
ρω1σf′ must be taken into account for its fatigue reliability analysis. Moreover, the proposed framework based on the strength‐damage interference provides more reasonably correlations with its field number of flights rather than the load‐life interference one.
In this paper, the effects of process‐induced voids and surface roughness on the fatigue life of an additively manufactured material are investigated using a crack closure‐based fatigue crack growth ...model. Among different sources of damage under cyclic loadings, fatigue because of cracks originated from voids and surface discontinuities is the most life‐limiting failure mechanism in the parts fabricated via powder‐based metal additive manufacturing (AM). Hence, having the ability to predict the fatigue behaviour of AM materials based on the void features and surface texture would be the first step towards improving the reliability of AM parts. Test results from the literature on Inconel 718 fabricated via a laser powder bed fusion (L‐PBF) method are analysed herein to model the fatigue behaviour based on the crack growth from semicircular/elliptical surface flaws. The fatigue life variations in the specimens with machined and as‐built surface finishes are captured using the characteristics of voids and surface profile, respectively. The results indicate that knowing the statistical range of defect size and shape along with a proper fatigue analysis approach provides the opportunity of predicting the scatter in the fatigue life of AM materials. In addition, maximum valley depth of the surface profile can be used as an appropriate parameter for the fatigue life prediction of AM materials in their as‐built surface condition.
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•Uniaxial strain- and force-controlled fatigue tests are conducted on LB-PBF 304L SS.•Specimens in machined/polished and as-built surface conditions are characterized.•Locational and ...directional effects on surface roughness of specimens are minimal.•The effect of surface roughness is more significant on stress-life behavior.•Fatigue life of as-built specimens can be estimated using their surface topography.
The fatigue strength of additively manufactured metallic parts in their as-built surface condition is mainly dominated by the surface roughness. Post-processing is often inevitable to reduce surface roughness effects even though post-processing diminishes the main advantage of additive manufacturing, which is net-shaped direct-to-service production. This study investigates the underlying mechanisms responsible for fatigue failure of additively manufactured 304L stainless steel parts in as-built and machined/polished surface conditions. Both strain- and force-controlled, fully reversed fatigue tests were conducted to gain a comprehensive understanding of surface roughness effects on fatigue behavior. The sensitivity to surface roughness is shown to be dependent on the control mode, with stress-based fatigue tests showing greater sensitivity than strain-based fatigue tests. Moreover, the fatigue life estimation for as-built specimens was performed based on surface roughness parameters as well as the fatigue properties of the specimens in machined/polished surface condition of the material without using any fatigue data of specimens in as-built surface condition. Accordingly, the effect of surface roughness on the fatigue behavior could be estimated reasonably well in both strain-life and stress-life approaches.
•Monte Carlo simulation employed to deal with VHCF data sparsity.•A machine learning model with ease of implementation proposed for VHCF life prediction.•The model demonstrated good accuracy using a ...shallow neural network structure.
Few machine learning (ML) models were applied for very-high-cycle fatigue (VHCF) analysis and these methods encounter limitations in data sparsity and overfitting. The present work aims to overcome data sparsity and propose an easy-to-use and nonredundant ML model for VHCF analysis. Monte Carlo simulation (MCs) is run to enlarge dataset size and a ML method is proposed to investigate the synergic influence of defect size, depth, location and build orientation on Ti-6Al-4V. The coefficient factor that indicates the percentage variation between the predicted and experimental fatigue lives can reach up to 0.98, meaning that the model demonstrates good prediction accuracy.
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