Prediction Of Mechanical Properties Research Articles

Prediction of Physical and Mechanical Properties of Al2O3–TiB2–TiC Composites Using Design of Mixture Experiments

Prediction of Physical and Mechanical Properties of Al2O3–TiB2–TiC Composites Using Design of Mixture Experiments

Evaluation and Prediction of Mechanical Properties According to Welding Methods of Ni 825/A516-70N Clad Plates

In this study, the microstructures and mechanical properties of different methods of FCAW (Flux-cored arc welding) + FCAW or FCAW + GTAW (Gas tungsten arc welding) welding on Ni825/A516-70N clad plate were investigated, using a double U groove angle on a laboratory scale. The amount of specimen deformation with FCAW + FCAW welding was ~1.5 mm, and that with FCAW + GTAW welding was ~3.6 mm. In the Vickers hardness tests, the average hardness value of the FCAW + GTAW welding specimens tended to be higher, and the hardness of the steel showed the same value. Also, in the tensile tests, the maximum strength and elongation of the FCAW + GTAW specimens were found to be higher. The SEM analysis of the welding boundaries of each specimen revealed the phenomenon of grain refinement in the specimens with FCAW + GTAW welding. This was due to the slow welding speed of this welding process and the rapid cooling rate caused by the relatively low layer thickness despite the relatively high number of weld passes. The computer simulation of the amounts of deformation were similar, with a value of 1.5 mm for FCAW + FCAW and 3.6 mm for the computer simulation, indicating the credibility of the simulation. In the computational simulation of butt welding, FCAW + GTAW showed high values for both residual stress and angular strain. The L-seam and C-seam of the demo vessel size were welded under the same conditions as the actual welding. The residual stress and angular deformation in the L-seam were 849 MPa and 2.3 mm for the FCAW + FCAW and 852 MPa and 2.9 mm for FCAW + GTAW. The residual stress and angular deformation in the C-seam were 920 MPa and 0.7 mm for FCAW + FCAW and 930 MPa and 0.9 mm for FCAW + GTAW, respectively.

Assessment of Classical Force-Fields for Graphene Mechanics

The unique properties of graphene have attracted the interest of researchers from various fields, and the discovery of graphene has sparked a revolution in materials science, specifically in the field of two-dimensional materials. However, graphene synthesis’s costly and complex process significantly impairs researchers’ endeavors to explore its properties and structure experimentally. Molecular dynamics simulation is a well-established and useful tool for investigating graphene’s atomic structure and dynamic behavior at the nanoscale without requiring expensive and complex experiments. The accuracy of the molecular dynamics simulation depends on the potential functions. This work assesses the performance of various potential functions available for graphene in mechanical properties prediction. The following two cases are considered: pristine graphene and pre-cracked graphene. The most popular fifteen potentials have been assessed. Our results suggest that diverse potentials are suitable for various applications. REBO and Tersoff potentials are the best for simulating monolayer pristine graphene, and the MEAM and the AIREBO-m potentials are recommended for those with crack defects because of their respective utilization of the electron density and inclusion of the long-range interaction. We recommend the AIREBO-m potential for a general case of classical molecular dynamics study. This work might help to guide the selection of potentials for graphene simulations and the development of further advanced interatomic potentials.

Machine learning prediction of mechanical properties of bamboo by hemicelluloses removal

The use of bamboo as a sustainable material is becoming increasingly prevalent; however, the optimisation of its mechanical properties remains a significant challenge. In this study, different concentrations of sodium hydroxide (NaOH) were used to remove the hemicellulose of bamboo and the effect of mechanical properties such as modulus of elasticity, flexural strength and elastic limit were evaluated. A total of 90 samples of data were collected and five machine learning algorithms including Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Ridge Regression (RR), Lasso Regression (LR) and Elastic Network Regression (ENR) were employed to build predictive models based on these properties. The models were trained and tested using 5-fold cross validation. The results showed that the mechanical properties of elastic modulus and elastic limit of bamboo medium were enhanced to 9.63 GPa and 66.44 MPa treated by 10 % NaOH, while the flexural strength of bamboo medium was up to 147.29 GPa with 5 % NaOH. The Ridge Regression algorithm was the best compared to other four algorithms. This methodological approach is not only offers an efficient way to optimize the mechanical properties of bamboo, but also enhances sustainable practices in the environmental sector.

A Review of Particle Packing Models and Their Applications to Characterize Properties of Sand-Silt Mixtures

This paper reviews particle packing models and explores their application in geotechnical engineering, specifically for sand-silt mixtures. The review covers key models, including limiting case, linear, and non-linear packing models, focusing on their mathematical structures, physical principles, assumptions, and limitations through the concept of excess free volume. The application of particle packing models in geotechnical engineering is explored in characterizing the properties of sand-silt mixtures, offering insights into maximum, minimum, and critical void ratios and inter-granular void ratio, and the prediction of mechanical properties.

Additive manufacturing of gradient porous Si/SiC ceramic parts: Quasi-static behaviors and mechanical properties

Porous silicon carbide (SiC) ceramic exhibits low density, high toughness, which endow it with an indispensable role in engineering applications. However, the manufacturing, designing, and making full use of the rich pore structure of gradient porous SiC ceramic to improve its mechanical performance still face many challenges. Herein, the manufacture of gradient porous Si/SiC ceramic part is realized for the first time, and the influence of gradient structural on mechanical properties is deeply analyzed. The results indicate that the porous Si/SiC ceramics with constant gradient transition rate is characterized by step-by-step destruction and can carry larger strains than the porous Si/SiC ceramics with non-constant gradient transition rate. Meanwhile, reducing the gradient span can improve the strength, but it is easy to lead to brittle damage. In particular, gradient porous Si/SiC ceramics with constant gradient transition rate and 30 % gradient span can exhibit both good strength and toughness. The compressive strength can reach 11.71 MPa, and the equivalent elastic modulus can reach 2.28 GPa. Finally, a laminar material prediction model for mechanical properties is presented. This paper presents an effective fabrication method for gradient porous ceramic parts and provides a feasible means for the design and prediction of mechanical properties.

Mechanical Property Prediction in Silica Fume and Crumb Rubber–Modified Concrete: Soft Computing and Experimental Approach

Mechanical Property Prediction in Silica Fume and Crumb Rubber–Modified Concrete: Soft Computing and Experimental Approach

Thermodynamic calculation-assisted design of 500 MPa high performance steel by machine learning

In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). Thermodynamic calculation has been used to obtain a large amount of data on phase fractions and mechanical properties for various chemical composition at different cooling rates. By comparing the predictive capabilities of different ML models, the deep neural network (DNN) and MOGA were combined to design Q500 wind power steel. Subsequently, three groups of designed chemical compositions were appropriately selected for verification. The results indicate that the yield strength of the experimental steel was over 500 MPa, and the maximum error in microstructure and mechanical property prediction was 5.0%. The prediction results demonstrated the rationality of the composition design framework. Meanwhile, the method of combining the material database and ML proposed in this study can also be applied to the design of other low-alloy steels.

Prediction of mechanical properties of basalt fiber concrete using hybrid recurrent neural networks based on freeze-thaw damage quantification

Prediction of mechanical properties of basalt fiber concrete using hybrid recurrent neural networks based on freeze-thaw damage quantification

An adaptive spherical indentation test integrating targeted testing scenarios, data acquisition, and model selection for uniaxial mechanical property predictions

An adaptive spherical indentation test integrating targeted testing scenarios, data acquisition, and model selection for uniaxial mechanical property predictions

Optimizing prediction of stainless steel mechanical properties with random forest: a comparison of feature selection methods

In machine learning, predicting the mechanical properties of stainless steel, such as Yield Strength (YS), Ultimate Tensile Strength (UTS), and Elongation (EL), requires many input variables, such as chemical composition, type of heat treatment, heating duration, and cooling method. However, the complexity and number of these variables can increase processing time and reduce model accuracy. This study aims to explore the impact of selecting the most influential input variables to improve prediction accuracy. We compared two feature selection techniques: Recursive Feature Elimination (RFE), which systematically removes less important features, and Information Gain (IG), which measures the contribution of each variable to the target prediction. Both techniques were implemented using the random forest algorithm, chosen for its robustness in handling large datasets and its ability to capture complex interactions between variables. Parameter optimization was performed using a grid search. The analysis showed that the RFE-based model outperformed both the IG-based model and the model without feature selection. In predicting YS, RFE identified 13 out of 21 influential variables, achieving a Mean Absolute Error (MAE) of 9.91, Root Mean Square Error (RMSE) of 14.20, and R-squared value of 0.89. For UTS, RFE identified 8 out of 21 variables, with an MAE of 12.89, RMSE of 16.97, and R-squared of 0.97. In predicting EL, RFE identified 14 out of 21 variables, with an MAE of 3.82, RMSE of 6.10, and an R-squared value of 0.85. The high R-squared values (0.85) across all properties indicate the model’s strong predictive capabilities, making it suitable for practical applications in predicting the mechanical properties of stainless steel.

Estimation of hydrides structure and mechanical properties of irradiated E110 alloy after thermomechanical tests imitating dry storage conditions

Safety assurance of spent nuclear fuel dry storage requires reliable prediction of mechanical properties of fuel element cladding depending on structural changes at all stages of nuclear fuel handling. In this work investigations of hydrides structure in irradiated fuel element cladding made of E110 alloy have been conducted in irradiated state as well as after tests simulating dry storage conditions. Using the program code, the coefficients that best correlate with mechanical properties have been found.

Prediction of single cell mechanical properties in microchannels based on deep learning

Traditional methods for measuring single-cell mechanical characteristics face several challenges, including lengthy measurement times, low throughput, and a requirement for advanced technical skills. To overcome these challenges, a novel machine learning (ML) approach is implemented based on the convolutional neural networks (CNNs), aiming at predicting cells’ elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels. In the present study, the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus, incorporating three widely-used constitutive models that characterize the cellular mechanical behavior, i.e., the Mooney-Rivlin (M-R), Neo-Hookean (N-H), and Kelvin-Voigt (K-V) models. Utilizing this dataset, a multi-input convolutional neural network (MI-CNN) algorithm is developed by incorporating cellular deformation data as well as the time and positional information. This approach accurately predicts the cell elastic modulus, with a coefficient of determination R2 of 0.999, a root mean square error of 0.218, and a mean absolute percentage error of 1.089%. The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R2 of 0.99, even when the stochastic noise is added to the simulated data. One significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied. The model accurately and reliably predicts single-cell mechanical properties, showcasing a robust ability to generalize. We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm’s accuracy and generalization. This algorithm presents a possibility for high-throughput, highly automated, real-time, and precise characterization of single-cell mechanical properties.

Prediction of Mechanical and Tensile Properties of Self-Compacting Concrete Incorporating Fly Ash and Waste Copper Slag by Artificial Neural Network-ANN

Prediction of Mechanical and Tensile Properties of Self-Compacting Concrete Incorporating Fly Ash and Waste Copper Slag by Artificial Neural Network-ANN

Surface Mechanical Property Prediction and Process Optimization of 18CrNiMo7-6 Carburized Steel Stator Guide Based on Radial Basis Function Neural Network and NSGA-II Algorithm

The carburizing process is a key technology that affects the mechanical properties of the surface of the hydraulic motor stator guide rail, and the related process parameters have an important influence on surface hardness, the thickness of the carburized layer, and the deformation of the guide rail. However, at present, the relationship between the carburizing process parameters and the surface mechanical properties of the target is not clear. This paper proposes a “hardness prediction and process parameter optimization” method. Firstly, a finite element model is established, with carburizing time, temperature, and carbon potential as the three input factors; the optimal Latin hypercubic experimental design and sensitivity analysis are applied. Secondly, surface hardness, carburized layer thickness, and deformation are taken as the output values, and an RBF neural network is used to construct the prediction model. The results show that the RBF neural network can be accurately used for the prediction of surface hardness, the thickness of the carburized layer, and deformation, and for the optimization of process parameters. The optimized parameters of surface hardness and the thickness of the carburized layer were increased by 4.2% and 5.1%, respectively, and the deformation amount was reduced to 0.31 mm, achieving the goal of optimal design.

Characterisation and prediction of mechanical properties in laser powder bed fusion-printed parts: a comparative analysis using machine learning

ABSTRACT This study investigates the effects of process parameters including scanning strategy, build orientation, and hatching distance on the mechanical properties of AlSi10Mg parts produced by Laser Powder Bed Fusion (L-PBF). The experiment varied these parameters within defined ranges and used statistical analysis to evaluate their impact on tensile strength and ductility. Results showed that scanning strategy had the greatest influence, followed by hatching distance, while build orientation affected anisotropic properties. Microstructural analysis showed clear correlation between process conditions and mechanical strength, thereby showing the underlying mechanisms that govern material behavior. Moreover, Machine learning models, including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs), were applied to predict tensile strength and ductility characteristics. RFR and SVR outperformed ANNs, showing high predictive accuracy with limited datasets. These findings emphasize the importance of optimizing L-PBF process parameters to minimize anisotropy and achieve consistent mechanical properties in produced parts.

The first-principles prediction of mechanical, electronic, elastic and thermodynamic properties of Mo2XB2 (X=Nb, Os, Ta)

The mechanical, electronic and elastic properties of Mo2XB2 (X = Nb, Os, Ta) at different pressures (0–50 GPa) were predicted using first-principles calculations. The enthalpy of formation and the elastic constant calculations show that the three transition metal borides are thermodynamically and mechanically stable. The analysis of density of state and band structure reveals that Mo2XB2 (X = Nb, Os, Ta) have metallic and covalent properties. The increase of pressure will increase the elastic modulus of Mo2XB2 (X = Nb, Os, Ta). The elastic modulus of Mo2XB2 (X = Nb, Os, Ta) are anisotropic. Furthermore, the quasi-harmonic Debye model is used to predict thermodynamic parameters at various temperatures (0–1200K) and pressures (0–50 GPa), such as heat capacity at constant pressure, heat capacity at constant volume, coefficient of thermal expansion, Debye temperature, and the Grüneisen parameter.

Effect of Porosity and Gradient Parameters on Compressive Mechanical Properties of Sheet Gyroid Gradient Porous Structures and Construction of Mechanical Properties Prediction Model

A series of uniform porous structures and radial stepwise and radial linear gradient structures are designed based on sheet‐gyroid to explore the influence mechanism of porosity and gradient parameters on forming quality and compressive mechanical properties. On this basis, mathematical models for predicting mechanical properties of porous structures based on uniform, discrete gradient, and linear gradient porosity distribution are established. The volume fraction deviation of uniform porous structures increases gradually with the increase of porosity. The relative density of porous structures ranges from 96.78 to 98.79%. The influence of porosity on compressive mechanical properties is investigated, and a prediction model of the equivalent elastic modulus and yield strength of porous structures is constructed based on the generalized G‐A model. The elastic modulus of the radial stepwise gradient porous structures is predicted by combining the rule of mixing. The deviation between the predicted results and the experimental results ranges from 0.21 to 2.61%. At the same time, a prediction model of the compressive mechanical properties of the radial linear gradient porous structure is constructed, and it is found that the predicted value is somewhat different from the experimental value, which is related to the synergistic strengthening effect of the gradient porous structure.

Prediction of Mechanical Properties of Hot-Rolled Strip Steel Based on XGBoost and Metallurgical Mechanism

Prediction of Mechanical Properties of Hot-Rolled Strip Steel Based on XGBoost and Metallurgical Mechanism

Prediction of Mechanical Properties of Rare-Earth Magnesium Alloys Based on Convolutional Neural Networks.

Rare-earth magnesium alloys exhibit higher comprehensive mechanical properties compared to other series of magnesium alloys, effectively expanding their applications in aerospace, weapons, and other fields. In this work, the tensile strength, yield strength, and elongation of a Mg-Gd-Y-Zn-Zr rare-earth magnesium alloy under different process conditions were determined, and a large number of microstructure observations and analyses were carried out for the tensile specimens; a prediction model of the corresponding mechanical properties was established by using a convolutional neural network (CNN), in which the metallographic diagram of the rare-earth magnesium alloy was taken as the input, and the corresponding tensile strength, yield strength, elongation, and three mechanical properties were taken as the output. The stochastic gradient descent (SGD) algorithm was used for parameter optimization and experimental validation, and the results showed that the average relative errors of the tensile strength and yield strength prediction results were 1.90% and 3.14%, respectively, which were smaller than the expected error of 5%.