•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.
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.
•A multi-layer perceptron with maximum entropy algorithm for probabilistic fatigue life prediction is proposed.•The paradigm of learning distribution models parameter in machine learning-based methods had been revolutionized.•A hybrid Loss function is constructed to guide the parameter updating of network.•The performance of the model was evaluated using three metrics on two datasets.
•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.
•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.
•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.
The most popular additive manufacturing (AM) technologies to produce titanium alloy parts are electron beam melting (EBM), selective laser melting (SLM) and directed energy deposition (DED). This ...investigation explores mainly these three techniques and compares these three methods comprehensively in terms of microstructure, tensile properties, porosity, surface roughness and residual stress based on the information available in the literature. It was found that the microstructure is affected by the highest temperature generated and the cooling rate which can be tailored by the input variables of the AM processes. The parts produced from EBM have strength comparable to that of conventionally fabricated counterparts. SLM and DED yield superior strength, which can be up to 25% higher than traditionally manufactured products. Due to the presence of larger tensile residual stress, surface roughness and porosity, AM fabricated parts have lower fatigue life compared to those of from traditional methods. EBM parts have slightly lower fracture toughness (i.e., lower fatigue life) than conventionally produced parts while SLM and DED have significantly lower fracture toughness. Annealing, hot isostatic pressing, stress relief and additional machining processes improve the characteristics of parts produced from AM. Ti–6Al–4V alloy parts fabricated via AM may have limited applications despite the high demands in aerospace or biomedical engineering. Since rapid product development using 3D printers leads to significant cost reductions more recently, it is expected that more opportunities may soon be available for the AM of titanium alloys with newer AM processes such as cold spray additive manufacturing (CSAM) and additive friction stir deposition (AFSD).
Without post-manufacture HIPing the fatigue life of electron beam melting (EBM) additively manufactured parts is currently dominated by the presence of porosity, exhibiting large amounts of scatter. ...Here we have shown that the size and location of these defects is crucial in determining the fatigue life of EBM Ti-6Al-4V samples. X-ray computed tomography has been used to characterise all the pores in fatigue samples prior to testing and to follow the initiation and growth of fatigue cracks. This shows that the initiation stage comprises a large fraction of life (>70%). In these samples the initiating defect was often some way from being the largest (merely within the top 35% of large defects). Using various ranking strategies including a range of parameters, we found that when the proximity to the surface and the pore aspect ratio were included the actual initiating defect was within the top 3% of defects ranked most harmful. This lays the basis for considering how the deposition parameters can be optimised to ensure that the distribution of pores is tailored to the distribution of applied stresses in additively manufactured parts to maximise the fatigue life for a given loading cycle.
•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.
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