Prediction Of Yield Stress Research Articles

Application of lubrication theory to determine yield stress in slump tests

The slump test can determine the yield stress of slurries by measuring the slump height. The convex shape in the slumped slurry profile cannot be characterised by the Tresca criterion. Lubrication theory has been widely applied to analyse the slow flow of yield stress fluids. Thus, the present study incorporates lubrication theory and the Tresca criterion to establish a generalized model for reconstructing the slurry profile based on the yield stress. The convex profile in the lower zone can be well represented by the novel method. The predicted yield stresses by the slump tests are larger than those determined using the vane method. Slump height and spread are equally accurate for predicting yield stress via the proposed method. Overall, the combination of lubrication theory and Tresca criterion yields a good prediction of yield stress based on slump height.

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

Yield stress Measurement of municipal sludge: A comprehensive evaluation of testing methods and concentration effects using a rotational rheometer

Accurate prediction and measurement of yield stress are crucial for optimizing sludge treatment and disposal. However, the differences and applicability of various methods for measuring yield stress are subjects of ongoing debate. Meanwhile, literature on measuring sludge yield stress is limited to low solid concentrations (TS <10%), understanding and studying the yield stress of medium to high solid concentration sludge is crucial due to increasingly stringent standards for sludge treatment and disposal. So, this study employed a rotational rheometer to measure sludge yield stress across a wide range of TS (4–50%) using steady shear, dynamic oscillatory shear, and transient shear. The study derived significant conclusions by comparing and summarizing the applicability and limitations of each testing method: Dynamic oscillatory shear methods, including G′-σ curve method, γ-σ curve method, and G**γc method can measure sludge yield stress ranging from 4% to 40% TS, while other methods are restricted to low or limited solid concentrations; The G' = G″ method, utilizing the intersection of G′ and G″ curves, consistently yields the highest value for yield stress when 4%≤ TS ≤ 12%; The rotational rheometer cannot measure sludge yield stress when the solid concentration exceeds 40% TS; The relationship between sludge yield stress and solid concentration is stronger as a power-law for TS ≤ 25%, transitioning to linear for higher concentrations (28%≤ TS <40%). This study systematically explores the applicability and limitations of various measurement methods for characterizing sludge yield stress across a wide range of solid concentrations, providing valuable guidance for scientific measurement and highlighting challenging research issues.

An improved meta heuristic IT2 fuzzy model for nondestructive failure evaluation of metal additive manufacturing lattice structure

Metal Additive Manufacturing (AM) lattice structure is widely used in various fields because of its lightweight and one-time forming characteristics. Nondestructive reliability evaluation still presents a challenge that requires immediate attention, especially in light of the service setting and when structural failure is used as an evaluation criterion. In this paper, the failure evaluation problem of structure is discussed based on the defects that affect yield stress, and the yield stress as the index. Firstly, a Computed Tomography (CT) scanner is used for non-destructive testing of metal AM lattice structure samples, and improved YOLO V7 is used to identify and count defects in CT scan images. The interval type 2 (IT2) fuzzy model is then enhanced using the irregular Gaussian function, and a meta-heuristic algorithm (particle swarm optimization) is added to enhance the model’s predictive capabilities. Finally, simulation and experiment are used to validate the AM lattice structure’s failure evaluation. The improved model’s superiority in the simulation exercise is demonstrated by contrasts with IT2 and the Type 1 fuzzy model. The yield stress prediction deviation of the sample during application verification is 1.12%, which attests to the validity of the failure prediction technique suggested in this paper.

Yield stress prediction from 3D reconstruction of fresh concrete slump

Relating the empirical slump test to yield stress facilitates the capture of fresh state concrete performance in fundamental quantitative terms whilst avoiding the requirement for expensive rheological equipment. This paper proposes a novel method to predict yield stress from slump by directly measuring the height of the unyielded region, via a 3D reconstruction approach. The efficacy of the proposed method is assessed through an experimental series of 21 tests. The results indicate a better correlation between the height of the unyielded region and yield stress compared to total slump height and yield stress. The proposed 3D reconstruction methodology predicts yield stress with significantly increased accuracy, indicated by a mean prediction error an order of magnitude lower than the average performance of existing models. The results of this study suggest that, for the first time, a valuable fundamental rheological property can be confidently derived from a standard concrete slump test.

Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid.

This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially 'black-box' in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings.

Intelligent Prediction of Dynamic Yield Stress in 3D Printing Concrete Based on Machine Learning

Intelligent Prediction of Dynamic Yield Stress in 3D Printing Concrete Based on Machine Learning

An Empirical Model for Accurate Prediction of Yield Stress by Slump Flow Based on a Mini-cylindrical Mold

An Empirical Model for Accurate Prediction of Yield Stress by Slump Flow Based on a Mini-cylindrical Mold

Prediction of yield stress of waxy gels in pipelines using scaling theory and crystallization kinetics

The development of a novel coupled kinetic and rheological model for paraffinic wax oil gelation is presented. The crystallization kinetics was studied by differential scanning calorimetry, and the rheological properties were obtained by dynamic oscillatory tests. The mass fraction of the crystallized wax, critical elastic strain, storage modulus in the Hookean region, and yield stress were obtained for model oils with 2.5, 5.0, and 7.5 wt% wax at cooling rates of 0.5, 0.75, and 1.0 °C/min. By applying such cooling range to the waxy oils, it was possible to verify the effect of this variable on fractal dimensional analysis using well-known scaling models for the first time. The scaling models used were successfully applied to conciliate the new gathered data enabling the discussion of their microstructural characteristics. From the kinetic and rheological behavior observed, simulations of waxy oil gelation during a pipeline shutdown were also performed. The yield stress evolution was estimated from zero to 14 days of quiescent cooling. With the aid of this novel methodology, the pressure needed to restart the flow and the time for maintenance services without pipeline blockage could be estimated based on the radial yield stress profile, providing a more accurate measurement than the direct extrapolation of yield stress measurements for field pipelines.

Effect of varying shear rates at different resting times on the rheology of 3D printable concrete

3D printing offers a paradigm shift in the construction industry. The material undergoes different stages like mixing, pumping, extruding, and printing with disparate shearing patterns and the effect of shear rates must be brought into consideration. The rheological properties of 3D printable concrete with varying shear rates at different resting times were studied. The designed mix has an ultra-high compressive strength of 110 MPa. Different hystresis curves with shear rates of 0–25 s−1 (P1) and 0–50 s−1 (P2) are studied. The tests were conducted at 10 min intervals up to 30 min resting time. The experimental down flow curves are further analyzed using the Bingham model, the modified Bingham model, and the Herschel-Bulkley model. It is observed that the rheological properties of 3D printable concrete are highly dependent on the resting time, and the applied shear rates and demand judicious selection of rheological models to predict yield stress and plastic viscosity. The comparison between experimental and analytical models indicates better predictability with the modified Bingham and Herschel- Bulkley model. The yield stress values for the modified Bingham model were found to be varying from 95 Pa to 178 Pa and Herschel-Bulkley model from 130 Pa to 177 Pa for P1 and P2, respectively.

Multiaxial Plastic Deformation of Zircaloy-4 Nuclear Fuel Cladding Tubes

The following work is motivated by the desire to devise an internal pressure test that can mimic a displacement-controlled loading scenario and demonstrate how to apply the multiaxial stress and strain data from the test to develop an elastic/plastic constitutive model for a thin-walled tubular component. This is achieved by conducting simultaneous measurements of tangential and axial strain during the pressure test and integrating these strain measures into a feedback loop with the pressure controller. It is shown how data from such a test can be used to develop a large mechanical property data set relevant to biaxial loading conditions. The data obtained have high confidence evidenced by their low variability and alignment with other literature studies. Additionally, data from these internal pressure tests combined with full-tube axial tensile tests allow for the derivation of the Hill anisotropic yield function. The developed Hill yield function is validated by comparing the plastic strain ratios from the full-tube tension tests and by comparing the predicted yield stress in the tangential direction with measured values from ring tension tests in a previous study.

Anisotropic Hardening of TRIP780 Steel Sheet: Experiments and Analytical Modeling

By combining experimental and theoretical models, this research investigates the anisotropic hardening behaviors of TRIP780 steel. The specimens of TRIP780 steel were subjected to uniaxial tensile and bulging tests under different loading conditions to obtain hardening data. The experimental results show that the strength and plastic deformation of TRIP780 steel vary with the loading directions, which indicates that TRIP780 steel has anisotropy characteristics. In this paper, the dichotomous method is used to ensure the convexity of the Chen-coupled quadratic and non-quadratic (CQN) function. Comparing the predictions of the hardening behavior of the TRIP780 steel sheet by the Yld2000-2d, Stoughton-Yoon'2009 and Chen-CQN functions, the results show that the Chen-CQN function exhibits the advantages of simple numerical implementation and a more realistic prediction of yield stress compared to the former two, respectively. Comparing the prediction of Chen-CQN function with the experimental hardening data, the results show that the deviation between the experimental data and the experimental response given by the function is always within 3%, and this function maintains an accurate prediction under different stress states, indicating that the Chen-CQN yield function has accuracy and flexibility for the characterization of the yield surface of TRIP780 steel.

Influence of aging on mechanical properties of the femoral neck using an inverse method

Today, we are facing rapid aging of the world population, which increases the incidence of hip fractures. The gold standard of bone strength assessment in the laboratory is micro-computed finite element analysis (μFEA) based on micro-computed tomography (μCT) images. In clinics, the standard method to assess bone fracture risk is based on areal bone mineral density (aBMD), measured by dual-energy X-ray absorptiometry (DXA). In addition, homogenized finite element analysis (hFEA) constructed from quantitative computed tomography reconstructions (QCT) predicts clinical bone strength more accurately than DXA. Despite considerable evidence of degradation of bone material properties with age, in the past fifty years of finite element analysis to predict bone strength, bone material parameters remained independent of age. This study aims to assess the influence of age on apparent modulus, yield stress, and strength predictions of the human femoral neck made by laboratory-available bone volume fraction (BV/TV) and μFEA; and by clinically available DXA and hFEA. Using an inverse method, we test the hypothesis that FEA material parameters are independent of age. Eighty-six human femora were scanned with DXA (aBMD) and with QCT. The femoral necks were extracted and scanned at 16 μm resolution with μCT. The grayscale images were downscaled to 32 μm and 65 μm for linear and non-linear analyses, respectively, and segmented. The μFE solver ParOSolNL (non-linear) and a standard hFEA method were applied to the neck sections with the same material properties for all samples to compute apparent modulus, yield stress, and strength. Laboratory-available BV/TV was a good predictor of apparent modulus (R2 = 0.76), almost as good as μFEA (R2 = 0.79). However, yield stress and strength were better predicted by μFEA (R2 = 0.92, R2 = 0.86, resp.) than BV/TV (R2 = 0.76, R2 = 0.76, resp.). For clinically available variables, prediction of apparent modulus was better with hFEA than aBMD (R2 = 0.67, R2 = 0.58, resp.). hFEA outperformed aBMD for predictions of yield stress (R2 = 0.63 vs R2 = 0.34 for female and R2 = 0.55 for male) and strength (R2 = 0.48 vs R2 = 0.33 for female and R2 = 0.15 for male). The inclusion of age did not improve the multiple linear models for apparent modulus, yield stress, and strength. The resolution of the μFE meshes seems to account for most morphological changes induced by aging. The errors between the simulation and the experiment for apparent modulus, yield stress, and strength were age-independent, suggesting no rationale for correcting tissue material parameters in the current FE analysis of the aging femoral neck.

Solid solution strengthening in medium- to high-entropy alloys

A fundamental understanding of the strength of multi-component alloys relies on well-defined experiments accompanied by accurate modelling. Whilst much work has been done so far for equi-atomic alloys, little has been done to investigate the effect of solid solution strengthening for alloys with deliberately adjusted, non-equimolar composition that are varied in certain concentration steps, including particularly continuous changes between equimolar subsets of alloy systems. This systematic approach is a key tool to verify or falsify current theories for solid solution strengthening for highly concentrated alloys. Series of alloys where a fifth element is alloyed to an equi-atomic four component alloy were prepared from Au, Cu, Ni, Pd and Pt, respectively. All investigated alloys form a single-phase solid solution, which is proven on a wide range of length scale by means of XRD, SEM and APT measurements. The mechanical properties of the series are compared to the predicted yield stresses calculated upon the model of Varvenne et al., (2017). The present results highlight coincident and discrepant results between experiment and model.

Explainable Ensemble Learning Models for the Rheological Properties of Self-Compacting Concrete

Self-compacting concrete (SCC) has been developed as a type of concrete capable of filling narrow gaps in highly reinforced areas of a mold without internal or external vibration. Bleeding and segregation in SCC can be prevented by the addition of superplasticizers. Due to these favorable properties, SCC has been adopted worldwide. The workability of SCC is closely related to its yield stress and plastic viscosity levels. Therefore, the accurate prediction of yield stress and plastic viscosity of SCC has certain advantages. Predictions of the shear stress and plastic viscosity of SCC is presented in the current study using four different ensemble machine learning techniques: Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), random forest, and Categorical Gradient Boosting (CatBoost). A new database containing the results of slump flow, V-funnel, and L-Box tests with the corresponding shear stress and plastic viscosity values was curated from the literature to develop these ensemble learning models. The performances of these algorithms were compared using state-of-the-art statistical measures of accuracy. Afterward, the output of these ensemble learning algorithms was interpreted with the help of SHapley Additive exPlanations (SHAP) analysis and individual conditional expectation (ICE) plots. Each input variable’s effect on the predictions of the model and their interdependencies have been illustrated. Highly accurate predictions could be achieved with a coefficient of determination greater than 0.96 for both shear stress and plastic viscosity.

Electronic, nanomechanical and smart reversible phase transition behaviours of sputtered titanium oxide-vanadium oxide composite thin films

Crystalline vanadium oxide-titanium oxide (VO-TO) composite thin films of 197 nm are developed on quartz and silicon substrates by RF magnetron sputtering technique. A thorough structural investigation of deposited thin films is carried out by field emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) techniques. Differential scanning calorimetry (DSC) and temperature-based sheet resistance measurement techniques reveals the Smart (i.e., reversible) phase transition behaviour of VO-TO films at about 45 °C. Further selective depth analysis by XPS on the VO-TO films shows the variation of the Ti doping amount along the thickness. The top region of VO-TO thin film possess lower Ti species, while beyond 50 nm thickness from the surface possess comparatively rich Ti species. This graded Ti species along the depth also results in the variation of nanomechanical properties along the depth as confirmed by nanoindentation. The simulation through the finite element approach is also carried out to depict the experimental results of nanoindentation. The nanohardness and modulus of the presently developed VO-TO composite film were measured as 6.7 GPa and 158.7 GPa, respectively at 50 nm depth. Beyond 50 nm, nanohardness and modulus were 5.6 GPa and 116.6 GPa, respectively. Further, finite element simulation predicted yield stress value of 3.52 GPa at 50 nm depth whereas higher yield stress of 4.68 GPa was predicted beyond 50 nm.

Ab initio informed yield criterion across body-centered cubic transition metals

Plasticity of body-centered cubic (bcc) metals is atypical at low temperatures, caused by the important lattice friction of 1/2〈111〉 screw dislocations. In this paper, we present a yield criterion that can be used to predict yield stress and active slip systems in all bcc transition metals for any applied stress. The criterion is fully parametrized on ab initio calculations of 1/2〈111〉 screw dislocation glide properties, and is applied in the case of uniaxial loading to compare with tension and compression experiments at low temperature. The criterion describes most of the different plastic behaviors observed experimentally in different metals, i.e. the dependence of the yield stress and active slip system on the orientation of the loading axis, as well as the tension/compression asymmetry. We find that plastic yield generally occurs at lower stresses in tension than in compression but for some metals, in particular niobium, there are large regions of the stereographic projection where compression is easier.

Retraction Note: A novel PCA-DC-Bagging algorithm on yield stress prediction of RAFM steel

Retraction Note: A novel PCA-DC-Bagging algorithm on yield stress prediction of RAFM steel

Effect of polymer-polymer interactions on the flow behavior of some polysaccharide-based hydrogel blends

This study is the continuation of our previous work (Kopač, Abrami, et al., 2021) where the theoretical approach of polymer-polymer interaction to predict the crosslink density of hydrogels was introduced. This theory is further extended to the flow properties of hydrogels that allow the analysis of synergistic effect in hydrogel systems and the understanding of possible anomalous behavior of certain mixtures. Various hydrogel structures were prepared accordingly by blending scleroglucan, anionic nanocellulose, Laponite dispersions and alginate solution. The relationship between mechanical and flow properties of the hydrogel network was carefully studied and eventually described by mathematical model. The linear model equation to predict yield stress of hydrogels in relation to the crosslink density was designed showing a satisfactory agreement between experimental data and model predictions. The correlation was adjusted by defining a proportionality coefficient, representing the energy defined per moles of crosslinks that can be used to restore the deformation.

Prediction of rheological behavior of self-compacting concrete by multi-variable regression and artificial neural networks

To control the rheological behavior of self compacting concrete, this study attempts to provide predictive models using a multi-variable regression model and artificial neural networks such as machine learning techniques on a database of 59 samples (average of 354 specimens), collated from literature. The statistical significance of the model coefficients and the coefficient of determination are adopted as criteria to check the performance of the model on test data.Interaction terms such as slump flow diameter, V-Funnel flow time, and L-Box ratio were inferred from literature to improve the efficacy of the predictive modeling. Two approaches for the prediction of yield stress and plastic viscosity are proposed and validated.With an increase in slump flow diameter and V-Funnel flow time, there was a significant increase in yield stress and plastic viscosity from 2.1 Pa and 18.2 Pa.s to 98.6 Pa and 198.8 Pa. s, respectively. A significant decrease in these rheological parameters was noted with an increase in L-Box ratio from 0.5 to 1. Thus, the comparison between the two numerical methods proposed shows that, for datasets with only one output, the multi-variable regression presents an important advantage. Thus, the regression model for the plastic viscosity represents values of R of the order of 0.955, 0.968, and 0.916 in correlation with slump flow diameter, V-Funnel flow time, and L-Box ratio, respectively.