Prediction Of Mechanical Properties Research Articles

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

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%.

Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites

Prediction of Mechanical Properties of 3D Printed Particle-Reinforced Resin Composites

Efficient prediction of mechanical properties of Boron Nitride Nanosheets using artificial neural networks

This paper aims to evaluate the mechanical properties of Boron Nitride Nanosheets (BNNS) and their vital use in nano-electromechanical systems (NEMS). By employing molecular Dynamics (MD) simulation, modelling of the atomic structure was done. The mechanical response of BNNS under various parameters (strain rate, temperature, chirality and dimension) enabled the generation of a comprehensive data set that accurately represents their elastic properties. The dataset obtained from MD simulation was subsequently utilized to construct an artificial neural network (ANN) model, tailored to predict the Young’s modulus of BNNS accurately. This work aimed to improve the model’s efficiency by refining the design of ANN, which significantly reduces the computational time while maintaining higher accuracy predictions. The findings demonstrate precise and rapid prediction for developing components based on BNNS in NEMS. This paper establishes an analogy between in-depth atomistic simulations and real-world engineering applications presenting a new approach for precisely predicting the attributes of nanomaterials.

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.

Prediction of mechanical property of open-hole composite laminates using generalized regression neural network method

Prediction of mechanical property of open-hole composite laminates using generalized regression neural network method

Prediction of mechanical properties of manufactured sand polymer-modified mortar based on swarm intelligence algorithm

Because polymer-modified mortar (PMM) exhibits a complex and diverse composition, there is a complex nonlinear relationship between its mechanical properties and mix proportions that is challenging for conventional mechanical performance prediction methods to accurately predict. Consequently, in this study, a predictive model utilizing a backpropagation neural network (BPNN) was formulated, featuring a structure of 6–14-2. Simultaneously, three swarm intelligence algorithms were integrated—the ant colony optimization algorithm, grey wolf optimization algorithm, and bat optimization algorithm (BAT)—to collectively refine the optimization process of the BPNN prediction model. The model's input layer included cement (OPC), cellulose ether (CE), dispersible polymer powder (DPP), antifoam (AF), fly ash (FA), and tuff stone powder (SP), and the output layer consisted of the compressive and bond strengths. The model dataset comprised 520 samples (260 × 2), with 60 % (312) used for model establishment and 40 % (208) used for validation. Correlation matrix and principal component analyses were conducted on the dataset, along with a comparative analysis of the factors influencing the mechanical performance evaluation indicators. The results indicate that at 7 and 28 d, there was a positive correlation between the DPP and AF with the development of PMM mechanical properties. At 7 d, the SP and FA were negatively correlated with the compressive and flexural strength, whereas the CE was positively correlated with the bond strength. At 28 d, the OPC was negatively correlated with the compressive, bond, and flexural strengths, and positively correlated with the SP and FA. C3 represents the optimal mix proportion for the PMM, and considering the influence of all raw materials, F3 was identified as the comprehensive optimal mix proportion. The predictive performance evaluation indicators of the BAT–BPNN for the compressive and bond strengths were R2 = 0.980 and 0.942, MAE = 5.967 and 1.958, MAPE = 0.071 and 0.041, and RMSE = 4.686 and 1.594, respectively. BAT significantly improves the predictive accuracy of the BPNN model, enabling the prediction of PMM mechanical properties and providing a scientific basis for its mix proportion design.

Assessing melt pool temperature independence from hardness via photodiode-based plank thermometry (PDPT)

Directed Energy Deposition (DED) has emerged as a promising metal additive manufacturing technique due to its ability to create components comparable or superior to traditional fabrication. Although industrial adoption of this technology is underway, there exists a considerable need to improve the predictability of the components it manufactures to promote wider adoption of this technology. Currently, melt pool temperature is the primary metric for build monitoring and control. However, stronger correlations must be established between the process parameters, including melt pool temperature, and resulting material outcomes to determine if melt pool control is a sufficient metric to control part quality. The goal of this research is to establish correlations between laser power (W), scanning speed (mm/s), powder flow rate (g/min), average melt pool temperature (C), and hardness. The average melt pool temperature was measured through Photodiode-based Planck thermometry (PDPT) with dual on-axis photodiodes. To obtain the correlation between these factors, 54 samples of Inconel 718 were printed, cross-sectioned, and tested using a Vickers diamond indenter. The resulting correlations exhibited extremely low R2 values and validated the findings of previous studies in that process parameters are exceedingly poor predictors of component properties. Average melt pool temperature lacked a significant correlation with hardness, instead process scanning speed demonstrated a more significant impact on the final material hardness. Since hardness is largely dictated by average grain size which in turn is controlled primarily by solidification rate, solidification rate should be the focus of future work to control material properties. The results of this study indicate that melt pool thermal control is not a sufficient metric to control part quality, and a solution is needed that can oversee the build process in such a way that solidification rate can be monitored and controlled in addition or instead of melt pool temperature. Future work must be done to apply the PDPT methodology to real time calibrated indirect measurement of melt pool solidification rates to improve prediction and control of final part mechanical properties.

Predictions of mechanical properties of Fiber Reinforced Concrete using ensemble learning models

Fiber Reinforced Concrete (FRC) significantly improves the tensile, crack resistance, and durability of concrete by adding fiber materials, making it highly promising for applications in structural reinforcement, repair and new construction projects. The strength of FRC is a crucial indicator of its mechanical properties. This study utilizes ensemble learning techniques to predict and analyze the compressive strength, splitting tensile strength, and flexural strength of FRC. A total of 1589 sets of compressive strength data, 1137 sets of splitting tensile strength data, and 1061 sets of flexural strength data were collected from existing literature. Four ensemble models (GBDT, Xgboost, LightGBM, RF) were used to establish prediction models and analyze influencing factors for these three FRC mechanical properties, and the results were compared with traditional models. The results show that the ensemble models can effectively solve the FRC strength prediction problems compared to traditional models. The GBDT regression model performed best in predicting compressive and flexural strength, with R2 values of 0.939 and 0.867, respectively. The Xgboost regression model performed best in predicting splitting tensile strength, with an R2 value of 0.944. Further analysis using SHAP revealed that cement and water significantly impact FRC strength. The inclusion of fibers significantly affects the splitting tensile strength and flexural strength of FRC but has a minimal impact on compressive strength. The study not only demonstrates the feasibility of using machine learning algorithms for FRC strength prediction but also provides a fast and reliable means for FRC strength prediction, offering more reliable data support for the design and application of FRC materials, aiding practical engineering applications.

Introducing explainable artificial intelligence to property prediction in metal additive manufacturing

Industry demands not only accurate predictability, but also the ability to explain predictions made by machine learning models. In the same endeavor, this work introduces eXplainable Artificial Intelligence (XAI) to mechanical property prediction of additively manufactured materials. Historical data for as-built Ti-6Al-4 V alloy manufactured via selective laser melting (SLM) was mined to generate a comprehensive dataset. Robust Gaussian Process Regression (GPR) and Neural Network (NN) were built using a hefty 189 training data arguments. Alongside primary SLM process parameters, novel features such as sample porosity and build direction, which are known to have direct impact on strength, were also utilized. The optimized GPR model exhibited mean absolute errors of 23.9 MPa and 0.58 % when inferencing on test tensile strength and elongation, respectively. On the other hand, the optimized NN model performed slightly worse with errors of 28.24 MPa and 0.97 % for respective tensile properties. Beyond training and testing of the models, they were tested for explainability against different core levels of human-centric understanding put forth by XAI community. It was ultimately concluded that explainability may come at the cost of accuracy.

A multi-scale uncertainty quantification model encompassing minimum-size unit cells for effective properties of plain woven composites

The uncertainty quantification is crucial to the high-precision prediction of composites’ effective properties. However, the unclear input uncertainties of multiscale parameters, complex uncertainty propagations of inter-scale correlations, and unaffordable computational cost of massive simulations are three primary problems at present. In this work, an innovative model with high accuracy and feasible cost for the mechanical property prediction of plain woven composites is developed encompassing minimum-size unit cells and multi-scale uncertainty quantification. For the accuracy holding, an uncertainty analysis process consists of the traceability description, inter-scale propagation and quantification is established. The uncertainties of geometry are described by uniform distributions for fiber, fiber bundle and composite scales, respectively; that of constituent properties is described by normal distributions for fiber and matrix, and its propagations to bundle and composite scales are realized by Nataf transformation methods with the consideration of parameter correlations. For the cost control, minimum-size unit cells are formulated by exhaustive analysis of structural symmetries to reduce the computational cost without accuracy compromising for the single simulation, and 1/8 and 1/16 unit cells compared with traditional full-size ones are obtained. The evolution convergence for statistical uncertainties of effective properties is finally obtained with totally reduced computational cost of 89%.

Experimental Investigation and Machine Learning Prediction of Mechanical Properties of Rubberized Concrete for Sustainable Construction

Concrete is widely used in civil engineering applications and the natural aggregates which used in concrete are scarce, but its demand is increasing. The disposal of rubber tyres poses a significant environmental challenge, as their decomposition releases harmful chemicals into the soil and water bodies over many years. Decomposition of tyres should be done in a smart way and hence came the emergence of mixing recycled rubber crumbs into concrete as Rubberised Concrete (RC). This paper provides an in-depth analysis of the mechanical properties of concrete such as Compressive Strength (fck), Tensile Strength (ft), Flexural Strength (fcr) of 7, 14, 28 days in replacement of fine aggregate with fine rubber (FR), and Coarse Aggregate with Coarse Rubber (CR). The results indicate that RC is more suitable for structural applications, including Reinforced Concrete columns, beams, slabs, than conventional concrete. The primary objective of the article is to explore the potential use of recycled rubber crumbs in concrete, referred to as Rubberised Concrete (RC), and to analyze its mechanical properties such as compressive strength, tensile strength, and flexural strength over different curing periods. Additionally, machine learning (ML) based prediction model has been developed for various strength characteristics of concrete mixtures at 28 days. The hyperparameter optimization using Grid Search CV with fivefold cross-validation have been performed to obtain the best hyperparameters. The model’s performance is evaluated using metrics like MAE, MSE, RMSE, and R-squared values. Results reveal varying performances among different ML algorithms for predicting flexural, tensile, and compressive strengths.