Prediction Of Yield Strength Research Articles

Predicting Tensile Properties of AZ31 Magnesium Alloys by Machine Learning

AZ31 magnesium alloys from different suppliers usually have different chemistry and processing histories, causing variance in their mechanical properties. In this work, we establish the quantitative relationship between alloy compositions, processing parameters, and mechanical properties by machine learning. Based on the artificial neural network (ANN) and support vector machine (SVM) algorithms, two models were built using a dataset with 112 data. Both models achieved good accuracy in predicting yield strength (YS), ultimate tensile strength (UTS), and tensile elongation (EL). To test their generalization ability, a new AZ31 extruded alloy was fabricated, with its chemical composition and processing history being documented. The YS, UTS, and EL of this material were measured and compared with model predictions. Relative errors for YS, UTS, and EL were 5.4%, 23%, and 272% by the ANN model, and 28%, 25%, and 143% by the SVM model, respectively. The reasons for the overestimation of the mechanical properties are discussed.

Composition and process parameter dependence of yield strength in laser powder bed fusion alloys

Understanding the factors influencing yield strengthening in alloys processed by laser powder bed fusion (LPBF) is critical in designing new formulations, and for predicting the optimum parameters for their processing. In this work, a relationship between the heat input and strengthening and softening mechanisms is proposed for a titanium, nickel and stainless steel alloy (Ti-6Al-4V, IN718 and 316L, respectively). Maximum strength is obtained with increasing heat input in 316L stainless steel; whereas IN718 and Ti-6Al-4V require low heat inputs. The results demonstrate that yield strength can be described in terms of the normalised enthalpy. The variation in the yield strength of LPBFed alloys depends prominently on dislocation multiplication/annihilation at certain processing temperatures and thermal straining, which are alloy dependent; as well as on dislocation strengthening and heat dissipation during cooling, which are process dependent. These dependencies are modelled via well-known metallurgical approaches. The relative contribution of various strengthening mechanisms is revealed. The findings of this work can be used as a metric for the prediction and further improvement of yield strength based on the choice of LPBF process parameters and chemical composition.

High-Throughput Exploration of the Process Space in 18% Ni (350) Maraging Steels via Spherical Indentation Stress–Strain Protocols and Gaussian Process Models

Several challenges are encountered in the development of maraging steels with desired combinations of mechanical properties. These include the need to explore a large process design space, the time- and effort-consuming standardized testing protocols for property evaluations, and the lack of a formal design of experiments strategy that guides the selection of the process conditions for the next set of experiments based on a thorough analyses of the previously accumulated data. In this work, we explored the effect of the different combinations of the aging temperature and the aging time on the yield strength of 350-grade maraging steels using high-throughput protocols. For this purpose, a total of 21 small volumes of differently aged samples were produced and studied using spherical nanoindentation stress–strain protocols. Furthermore, the results of these tests were modeled using Gaussian process regression (GPR) to establish data-driven linkages between the yield strengths and the aging parameters. The predicted yield strengths were found to be in reasonable agreement with the experimentally measured ones. It is also demonstrated that the GPR model can provide objective guidance in the selection of the next set of experiments.

Microstructure entropy guided understanding of yield strength in alloy systems characterized by grain size distribution

We introduce and adopt the concept of a phenomenological parameter, microstructure entropy, S*, to understand the yield strength in alloy systems with bimodal grain size distribution obtained from large sets of experimental data. 60 groups of bimodal grain size distribution data was obtained through the application of an ingenious concept involving severe cold deformation to obtain dislocation cell-type of martensite, followed by annealing when martensite reverts to austenite by shear or diffusion mechanism, with the aim to obtain ultrafine grain size. Six factors emerged from statistical data analysis through EBSD studies in terms of grain size and grain numbers from 60 sets of data. Microstructure entropy (S*) was obtained from the bimodal structure data in a self-similar regime. Inverse of the square root of microstructure entropy (1S*) and yield strength exhibited a linear relationship. The proposed conceptual methodology has wide acceptance for any grain size distribution to obtain microstructure entropy (S*) of a specific grain structure and predict yield strength. A generic equation similar to Hall-Petch relationship, but involving microstructure entropy is proposed to predict and understand yield strength of metallic systems characterized by grain size distribution or microstructural evolution.

Yield strength and misfit volumes of NiCoCr and implications for short-range-order

The face-centered cubic medium-entropy alloy NiCoCr has received considerable attention for its good mechanical properties, uncertain stacking fault energy, etc, some of which have been attributed to chemical short-range order (SRO). Here, we examine the yield strength and misfit volumes of NiCoCr to determine whether SRO has measurably influenced mechanical properties. Polycrystalline strengths show no systematic trend with different processing conditions. Measured misfit volumes in NiCoCr are consistent with those in random binaries. Yield strength prediction of a random NiCoCr alloy matches well with experiments. Finally, we show that standard spin-polarized density functional theory (DFT) calculations of misfit volumes are not accurate for NiCoCr. This implies that DFT may be inaccurate for other subtle structural quantities such as atom-atom bond distance so that caution is required in drawing conclusions about NiCoCr based on DFT. These findings all lead to the conclusion that, under typical processing conditions, SRO in NiCoCr is either negligible or has no systematic measurable effect on strength.

Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels

Artificial intelligence is widely employed in metallurgy for its ability to solve complex phenomena, which are associated with the learning process of previously obtained experimental data. Although numerous physical modeling techniques have been implemented for the prediction of mechanical strength using equations, several empirical efforts are necessitated to evaluate specific constants for different models. To address this issue, numerous recent studies have employed artificial neural networks for the prediction of mechanical properties based on the material composition and process conditions; however, majority of these works have been limited applications due to the extensive number of input parameter combinations of chemical compositions. Microstructure is a good feature to understand mechanical properties because it incorporates the effects of material composition and process conditions. The complex combination of material composition and process parameters determines the microstructure of steel. In this study, the information on microstructural volume fraction is utilized for the prediction of tensile strength, yield strength, and yield ratio via artificial neural networking. Various combinations of PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), BF (bainitic ferrite), and M (martensite) are investigated for the prediction of yield strength, ultimate tensile strength, and yield of high strength steel via back-propagation linear regression and neural network based algorithm. The effects of each microstructure on the three mechanical properties were successfully predicted by employing back-propagation linear regression. A deep learning algorithm with hyper-parameter tuning and cross-validation enabled high accuracy in predicting experimental data with mean absolute percentage errors of 6.59% and 10.78% for the validation and test sets, respectively. These studies can open a new avenue for applying the microstructural design effects to find optimum yield strength, tensile strength, and yield ratio of high strength steel.

Prediction of yield strength in a polycrystalline nickel base superalloy during interrupt cooling

Two-step solution cooling experiment was performed to investigate the precipitation behavior in a polycrystalline nickel base superalloy. Both experimental and simulation results show that the particle size distribution (PSD) of γ′ precipitates strongly depends on the transition temperature (Ttrans) that is closely related to their nucleation driving force. We proposed a modified model that considers multi-modal PSD to predict the yield strength, which is numerically validated by the experimentally measured hardness.

The Influence of Texture on the Ductile-to-Brittle Transition Behavior in Fe20Cr4.5Al Oxide Dispersion Strengthened Alloy

This paper reports on hardness, tensile properties and notch impact bending toughness values of an Fe20Cr4.5Al oxide dispersion strengthened (ODS) alloy specifically processed to achieved different preferential orientations: random, <100>, <110> and <111> parallel to the bar axis. In spite of the differences in the grain size, it was found for <100>, <111> and random orientations that the mean hardness values on the transverse cross sections is not remarkably sensitive to the texture. On the other hand, a significantly different mean hardness value for the material having the <110> crystalline orientation was found. Regarding the yield strength, it was found for random, <100> and <111> orientations that the yield strength is proportional to the Taylor’s factor. The difference between experimental and predicted yield strength values for <110> orientation was attributed to the offset effect induced by the dislocation cell size. The variation of the cleavage fracture strength with the texture was analyzed in the basis of two criteria: one based on the Normal Stress Law (macroscopic nature), and the other based on the assumption that fracture occurs from the propagation of a microcrack-like defect (microscopic nature). In this sense, it was concluded from the fractographic evidences that random and <100> orientations follow a mechanism where the fracture kinks along of the cleavage plane from a penny shaped microcrack nucleated in a second phase particle, meanwhile in the <110> and <111> orientations the fracture propagation arises from a penny shaped defect on the cleavage plane. Finally, the lower shelf values determined for the conditions studied are the same regardless of the texture and microstructure. The effect of texture on the notch toughness was noted where plastic flow predominates, i.e., in the ductile to brittle transition temperature and in the upper shelf energy.

Artificial Intelligence for the Prediction of Tensile Properties by Using Microstructural Parameters in High Strength Steels

Artificial intelligence is widely employed in metallurgy for its ability to solve complex phenomena, which are associated with the learning process of previously obtained experimental data. Although numerous physical modeling techniques have been implemented for the prediction of mechanical strength using equations, several empirical efforts are necessitated to evaluate specific constants for different models. Microstructure is a good feature to understand mechanical properties because it incorporates the effects of material composition and process conditions. The complex combination of material composition and process parameters determine the microstructure of steel. In this study, the information on microstructural volume fraction is utilized for the prediction of tensile strength, yield strength, and yield ratio via artificial neural networking. Various combinations of PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), BF (bainitic ferrite), and M (martensite) are investigated for the prediction of yield strength, ultimate tensile strength, and yield of high strength steel via back-propagation linear regression and neural network based algorithm. The effects of each microstructure on the three mechanical properties were successfully predicted by employing back-propagation linear regression. A deep learning algorithm with hyper-parameter tuning and cross-validation enabled high accuracy in predicting experimental data with mean absolute percentage errors of 6.59% and 10.78% for the validation and test sets, respectively. These studies can open a new avenue for applying the microstructural design effects to find optimum yield strength, tensile strength, and yield ratio of high strength steel.

An integrated-model for austenite yield strength considering the influence of temperature and strain rate in lean steels

Due to the necessity to exhaustively control the hot deformation of austenite, a strong effort has been made in the past to propose predictive models for the yield strength. Such models have shown accurate predictions within relatively narrow compositional range, closely related to the limitations of the database employed. Nevertheless, by testing those models with a large database, including a wider range of temperatures and compositions, the authors have observed that such narrow compositional ranges cause these models to be inaccurate. The strain rate is also an important parameter that has been ignored in previous works, and deeply affects mechanical behavior. In this work, we propose a more robust model, which includes the contribution of the strain rate and wider composition and temperature ranges by integrating different formulations, providing improvement at predicting yield strength with respect to previous models.

Precipitation and Mechanical Property of V‐Alloyed Steel: Role of Cooling Rate

Herein, the effect of cooling rate on the precipitation behavior and mechanical properties of V‐added microalloyed steel is systematically investigated by nanoindentation test, X‐ray diffraction, and transmission electron microscopy. The present observation reveals that random precipitates and interphase precipitates are simultaneously observed at the low cooling rate of 0.5 °C s−1, whereas only random precipitates are obtained at the high cooling rate of 2 °C s−1. The contribution of the nanoscale precipitates to yield strength is evaluated to be 199 MPa at the low cooling rate of 0.5 °C s−1, which is almost 4 times higher than that of 2 °C s−1. The predicted yield strength by semiempirical model based on the structural parameters is consistent with the result from tensile testing. In addition, the greater difference between upper and lower yield points in sample B is caused by dislocations motion, which is related to the smaller interparticle spacing of nanosized precipitates within ferrite matrix.

Mechanical Properties and Strength Prediction of Ti Microalloyed Low Carbon Steel with Different Ti Content

The effects of Ti on microstructures, recrystallization and mechanical properties of microalloyed low carbon steels were studied. Hall-Petch relationship and Ashby-Orowan equation were applied to analyse the reason for the variation of strength. The results show that the recrystallization activation energy increases parabolically as the Ti content increases. Besides, the yield strength, tensile strength and the yield ratio increase in varying degrees with the increasing of Ti content, while the elongation decreases. The fine grain strengthening makes the greatest contribution to the strength, and the precipitation strengthening of TiC particles is the main reason why the strength of test steel increases significantly when the Ti content exceeds 0.042%. In addition, the strength prediction model was established for Ti microalloyed low carbon steel. The relative error of predicted yield strength and tensile strength of Q345B steel are within ± 8% and ± 7% respectively.

Yield-strength prediction of flattened steel pipes by competing Bauschinger effect and strain hardening during pipe-forming

Since flattened steel sheets often show the unexpectedly lower or higher yield strength than leveled sheets, unceasing efforts have been made to accurately predict the yield strength in pipe-forming industries. In the present investigation, the yield strength of line-pipe or casing-pipe steels was predicted by competing Bauschinger effect and strain hardening occurred during the pipe-forming. Yield drop (YD) and yield rise (YR) parameters were newly defined from cyclic simulation analyses of outer and inner walls of pipes to express more reasonably the Bauschinger effect and strain hardening. The YD increased abruptly until the pre-strain of about 1%, and then saturated, while the YR increased linearly with increasing pre-strain. By combining the YD and YR, the variation in yield strength (Δσ) showed a down-and-up behavior as the Bauschinger effect and strain hardening were dominant at low and high pre-strains, respectively, and plausibly explained the relationship of Δσ and piping strain used in pipe-forming industries. According to the microstructural analyses related to the down-and-up Δσ behavior, the polygonal ferrite reduced the yield-strength reduction in the low pre-strain range, whereas the granular bainite or pearlite expanded it. This yield strength prediction coupled with microstructural analyses provide a good idea for designing and reliably predicting the yield strength of in various steel pipes.

Numerical prediction of tensile yield strength and micro hardness of Ti6Al4V alloy processed by constrained bending and straightening severe plastic deformation

This paper presents a numerical prediction of tensile yield strength and hardness properties of Ti6Al4V alloy processed by constrained bending and straightening (CBS) severe plastic deformation (SPD) technique. The CBS method has been proposed to enhance continuous processing of metal sheets and improve magnitude and homogeneity of entailed tensile, hardness and strain properties. The CBS simulation was performed on Ti6Al4V alloy rectangular sheets at N = 2, N = 4 and N = 6 passes respectively denoted as N2, N4 and N6 each combined with F = 12 mm, F = 6 mm and F =4 mm feed lengths respectively denoted as F4, F6 and F12. ABAQUS Standard FEA Software was used to determine magnitude and homogeneity of induced equivalent plastic (EP) strain. Applying the power law strengthening equation, simulated EP values were used to predict magnitudes of tensile yield stress and hardness of the samples. Numerical results were validated with experimental results at N2, N4, N6 passes for F6, F12 feeds. Simulation results showed that magnitude and homogeneity values of EP strain, yield strength and hardness were the highest at N6F4 followed by those at N6F6 and N6 F12 respectively. Experimental results showed that the yield strength and hardness had the highest increases by 34.5% and 24.4% respectively at N4F6 over that of as received (AR) samples. A comparison between simulation and experimental results on yield strength and hardness showed direct corelation at N2, N4 passes. However a relative inverse corelation was observed at N6 pass due to saturation of hardening and onset of material excessive yield after N4 pass.

Microstructural characteristics and thermophysical properties of spark plasma sintered Inconel 738LC

Nickel-based superalloys are used for turbine component parts in the power industry, aircraft engines, and in the marine sector. One such alloy that is available for these purposes is Inconel 738LC (IN738LC) nickel-based superalloy. Therefore, it is pertinent to have good structural and thermal stabilities for high-temperature applications, since these characteristics are important for its performance. In this study, spark plasma sintering technology was used to fabricate IN738LC nickel-based superalloy by using sintering temperature in the range of between 900 and 1200 °C. The fabricated products were characterized by using the scanning electron microscope and X-ray diffraction. There was the formation of the γ′(Ni3(Ti, Al)) intermetallic phase in the microstructure, which contributed to the material (Inconel 738LC) property. The solid solution elements present are chromium, cobalt, tungsten, and tantalum; these equally contribute to the structural stability of IN738LC. The density, hardness, and the predicted yield strength increased with increasing sintering temperature, while the porosity decreased with increasing sintering temperature. The thermal conductivity, which is an important thermophysical property of IN738LC, enables the evaluation of the usefulness and service life of IN738LC superalloy at high-temperature applications, was also examined. The thermal conductivity was measured by using a laser flash method in the temperature range of 100–600 °C. For the sintered alloy, the thermal conductivity was determined as a function of the thermal diffusivity, specific heat capacity, and density. Analysis showed inflection pattern with increase in temperature on the sample fabricated at 900, 1000, and 1100 °C, ; a monotonic increase with increase in temperature was observed on the sample fabricated at 1200°C, ; the inflections are associated with the γ′ precipitates dissolution and redistribution.

Strengthening and Mechanical Properties of SiC and Graphite Reinforced Al6061 Hybrid Nanocomposites Processed Through Ultrasonically Assisted Casting Technique

Al6061 alloy-based hybrid nanocomposites reinforced with 2wt% SiC and x wt% of graphite (x = 0, 0.5, 1, 1.5, 2 and 3) nanoparticles are fabricated through ultrasonically assisted casting technique. Microstructure, phases, grain size and fracture surfaces of the hybrid nanocomposites are studied to understand the mechanical properties. Microstructural studies revealed the uniform distribution of SiC and graphite nano-reinforcements in the matrix. The small-scale clusters appeared in the microstructure with the increase in graphite nanoparticles. The grain size, density, hardness and ultimate tensile strength of hybrid nanocomposites decreased with the rise of graphite in the composite material. The yield strength of the hybrid nanocomposites increased with increase in graphite up to 2 wt% and then decreased. SiC and graphite dual phase nanoparticles’ strengthening effect on yield strength was theoretically evaluated using various strengthening mechanisms including porosity effect. Enhancement of yield strength in hybrid nanocomposite due to strengthening mechanisms followed the trend $$\Delta \sigma_{{\Delta {\text{CTE}}}} > \Delta \sigma_{\text{Orowan}} > \Delta \sigma_{\text{HP}} > \Delta \sigma_{\text{load}}$$ . The predicted yield strength of hybrid nanocomposites obtained using the modified Clyne model and quadratic summation model were close to the experimental values. Fracture surfaces of hybrid nanocomposites exhibited brittle fracture with interdendritic cracking, stepwise facets and particle pull out with the increase in graphite content in the matrix.

Prediction of Aging Kinetics and Yield Strength of 6063 Alloy

Bulk hardness and tensile properties of an AA6063 Al-Mg-Si alloy specimens subjected to artificial age-hardening treatment over a wide range of temperatures (373-523 K) and time durations (5 min to 60 days) have been determined and analyzed to predict complete aging kinetics and yield strength. The predictions have been made using simplified hardness-based models. The experimental results are used to generate kinetic models for under- and overaging regimes following the Johnson–Mehl–Avarami and the Lifshitz–Slyozov–Wagner equations, respectively. The obtained kinetic parameters are found to satisfactorily describe the overall aging kinetics of the Al-Mg-Si alloy. Additionally, relationship between the estimated hardness and the yield strength values of the AA6063 alloy has been established and its utility has been validated. The developed hardness-based models can be used as convenient tool to determine the state of aging and to predict the yield strength of Al-Mg-Si alloy.

Effects of cooling rate and strain rate on phase transformation, microstructure and mechanical behaviour of thermomechanically processed pearlitic steel

In the present investigation, thermomechanical controlled processing of a high carbon steel and a Nb microalloyed high carbon steel have been conducted in a Gleeble 3800 simulator. Different microscopic techniques have been utilised for the characterisation of the microstructure and hardness data has been used for the evaluation of mechanical properties. In order to suppress the transformation enthalpy, experiments are performed under varying cooling rate and strain rate. The effect of niobium microalloying leads to the lowering of recalescence and suppresses austenite to pearlite transformation start and finish temperatures at every cooling rate which leads to the refinement of interlamellar spacing and thereby improve hardness and predicted yield strength values. It is evident that a higher strain rate accelerates the kinetics of pearlite transformation and elevates the pearlite start temperature. The increase of strain rate in the range of 1s–1 to 100s–1 followed by a constant cooling rate (free cooling) leads to the refinement of interlamellar spacing as well as improves mechanical properties. The true stress-true strain diagram at a lower strain rate indicates higher strain hardening with sharp yield point, whereas the same at a higher strain rate indicates the sudden occurrence of strain softening. The variation in recalescence due to the alternation of the cooling rate and strain rate has been correlated with the final microstructure and mechanical properties.

Modelling of Mechanical Properties of Pearlitic Rail Steel

Railway has become more essential for both mass and goods transportation so that the rails are required to carry higher loads and exhibit longer lifetime. Thus, mechanical properties, especially strength and toughness of rail steel must be continuously increased. In the present work, microstructure, tensile properties and impact toughness of a pearlitic rail steel grade 900A were firstly characterized. It was found that the investigated steel showed high yield and tensile strengths, but moderate elongation. Subsequently, representative volume elements (RVE) model was employed to investigate the effects of bainitic phase on mechanical properties of pearlitic rail steels. The flow stress curves of the individual phases were defined with regard to the chemical composition. As a result, the relationships between predicted yield strengths and tensile strengths in dependence on the phase fraction of bainite were provided. The model can be used to identify the proper microstructure characteristic of rail steel.

BisQue for 3D Materials Science in the Cloud: Microstructure\u2013Property Linkages

Accelerating the design and development of new advanced materials is one of the priorities in modern materials science. These efforts are critically dependent on the development of comprehensive materials cyberinfrastructures which enable efficient data storage, management, sharing, and collaboration as well as integration of computational tools that help establish processing–structure–property relationships. In this contribution, we present implementation of such computational tools into a cloud-based platform called BisQue (Kvilekval et al., Bioinformatics 26(4):554, 2010). We first describe the current state of BisQue as an open-source platform for multidisciplinary research in the cloud and its potential for 3D materials science. We then demonstrate how new computational tools, primarily aimed at processing–structure–property relationships, can be implemented into the system. Specifically, in this work, we develop a module for BisQue that enables microstructure-sensitive predictions of effective yield strength of two-phase materials. Towards this end, we present an implementation of a computationally efficient data-driven model into the BisQue platform. The new module is made available online (web address: https://bisque.ece.ucsb.edu/module_service/Composite_Strength/) and can be used from a web browser without any special software and with minimal computational requirements on the user end. The capabilities of the module for rapid property screening are demonstrated in case studies with two different methodologies based on datasets containing 3D microstructure information from (i) synthetic generation and (ii) sampling large 3D volumes obtained in experiments.