Model For Prediction Of Mechanical Properties Research Articles

A model predicting mechanical properties of asphalt mixtures considering void ratio and loading conditions

The void ratio is an essential parameter in asphalt mixture design, which significantly affects asphalt mixtures' mechanical properties and road performance. The prediction model for mechanical properties will be established based on the Computed tomography (CT) scanning, strength, and fatigue test results in this research to reveal the relationship between the asphalt mixtures' mechanical properties and void ratio. Firstly, the voids' transverse and longitudinal distribution characteristics in asphalt mixtures of different gradations were investigated based on CT scanning. Then, the influences of void ratio, loading rates on the strength, and the impacts of void ratio and stress levels on the fatigue life were investigated by conducting indirect tensile tests. Finally, the mechanical properties prediction model was established for void ratio and loading conditions. Further, the feasibility of the model is verified in conjunction with existing research results. The results show that the void ratio calculated based on CT scanning is about 1.15 times the measured void ratio in the water weight method. The number of voids was uniformly distributed in the range of 15 mm∼50 mm in height and 10 mm∼40 mm in radius of the specimen. The asphalt mixtures' strength decreases with increasing void ratio and increases with increasing loading rate. The asphalt mixtures' fatigue life declines with increasing void ratio and stress level. The correlation coefficients of the established strength and fatigue prediction models were 0.963 and 0.976, respectively. The relative error between the measured strength, fatigue life, and predicted results is about 10.75% and 11.27%, respectively. This research provides a basis for designing high-performance asphalt mixtures by establishing the relationship between asphalt mixtures' microscopic and mechanical characteristics.

Prediction Models of Mechanical Properties of Jute/PLA Composite Based on X-ray Computed Tomography.

The tensile strength and modulus of elasticity of a jute/polylactic acid (PLA) composite were found to vary nonlinearly with the loading angle of the specimen through the tensile test. The variation in these properties was related to the fiber orientation distribution (FOD) and fiber length distribution (FLD). In order to study the effects of the FOD and FLD of short fibers on the mechanical properties and to better predict the mechanical properties of short-fiber composites, the true distribution of short fibers in the composite was accurately obtained using X-ray computed tomography (XCT), in which about 70% of the jute fibers were less than 300 μm in length and the fibers were mainly distributed along the direction of mold flow. The probability density functions of the FOD and FLD were obtained by further analyzing the XCT data. Strength and elastic modulus prediction models applicable to short-fiber-reinforced polymer (SFRP) composites were created by modifying the laminate theory and the rule of mixtures using the probability density functions of the FOD and FLD. The experimental measurements were in good agreement with the model predictions.

Prediction of mechanical properties of 3D tubular braided composites at different temperatures using a multi-scale modeling framework based on micro-CT

It is of great significance to establish a real three-dimensional (3D) tubular braided composites mechanical properties prediction model at different temperatures. In this paper, a multi-scale modeling framework based on micro-computed tomography (micro-CT) is adopted to consider the characteristics of the real yarn cross section, fiber shape deviation and internal defects within the matrix after composite formation, and a realistic trans-scale finite element model for 3D tubular braided composite is established. The micro-scale and macro-scale mechanical properties of 3D tubular braided composites at different temperatures are sequentially simulated by using the elastic-plastic damage model considering temperature and the tractor-separation constitutive model. Comparison with experiments shows that temperature significantly affects the mechanical properties. With the increase of temperature, the overall failure degree of the 3D tubular braided composite under axial compressive load increases significantly, its axial compressive strength and modulus decrease significantly, and the post-peak response of the stress-strain curve gradually flattens. The proposed trans-scale model demonstrates high predictive accuracy.

Intelligent prediction model of mechanical properties of ultrathin niobium strips based on XGBoost ensemble learning algorithm

Ultrathin niobium strips with different thicknesses are prepared by an accumulative rolling process. The tensile test of the ultrathin niobium strips is carried out, and the microstructure of each niobium strip is characterized by electron backscattered diffraction (EBSD). The process parameters, mechanical properties and microstructure characterization data of rolled ultrathin niobium strips with different thicknesses are collected, analyzed and sorted. A data-driven intelligent prediction model for the mechanical properties of ultrathin niobium strips is established by deeply integrating the mechanical properties of ultrathin niobium strips with the microstructure evolution mechanism and combining the integrated data with the XGBoost ensemble learning algorithm. The optimal parameters of the XGBoost model are determined by a grid search and used for mechanical performance prediction. The overall generalization performance of the model is evaluated by the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). To reflect the advancement of the proposed model, the prediction results of this model are compared with those of the random forest (RF), multi-layer perceptron (MLP) and gradient boosting decision tree (GBDT) prediction model. The research results show that, on the test set, the R2 in terms of the tensile strength and yield strength predicted values based on the XGBoost algorithm were higher than those of other models, reaching 0.944 and 0.964, respectively. The three error indicators corresponding to the XGBoost model were also at the lowest level. This means that the model based on the XGBoost algorithm has the optimal generalization performance and can thus realize the accurate prediction of the mechanical properties of ultrathin niobium strips. This research provides a new method and idea for the optimal design of rolling process and the prediction of their mechanical properties.

Bioactive and Biodegradable Polycaprolactone-Based Nanocomposite for Bone Repair Applications.

This study investigated the relationship between the structure and mechanical properties of polycaprolactone (PCL) nanocomposites reinforced with baghdadite, a newly introduced bioactive agent. The baghdadite nanoparticles were synthesised using the sol-gel method and incorporated into PCL films using the solvent casting technique. The results showed that adding baghdadite to PCL improved the nanocomposites' tensile strength and elastic modulus, consistent with the results obtained from the prediction models of mechanical properties. The tensile strength increased from 16 to 21 MPa, and the elastic modulus enhanced from 149 to 194 MPa with fillers compared to test specimens without fillers. The thermal properties of the nanocomposites were also improved, with the degradation temperature increasing from 388 °C to 402 °C when 10% baghdadite was added to PCL. Furthermore, it was found that the nanocomposites containing baghdadite showed an apatite-like layer on their surfaces when exposed to simulated body solution (SBF) for 28 days, especially in the film containing 20% nanoparticles (PB20), which exhibited higher apatite density. The addition of baghdadite nanoparticles into pure PCL also improved the viability of MG63 cells, increasing the viability percentage on day five from 103 in PCL to 136 in PB20. Additionally, PB20 showed a favourable degradation rate in PBS solution, increasing mass loss from 2.63 to 4.08 per cent over four weeks. Overall, this study provides valuable insights into the structure-property relationships of biodegradable-bioactive nanocomposites, particularly those reinforced with new bioactive agents.

Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm.

As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R2 = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R2 = 0.99, MAE = 2.65 MPa; elongation: R2 = 0.84, MAE = 1.42%; reduction in area: R2 = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions.

Hydrothermal aging of short glass fiber reinforced polyphenylene sulfide composites and property prediction

In this paper, hydrothermal aging behavior of short-cut glass fiber reinforced polyphenylene sulfide composites (PPS/GF) were studied. The water adsorption behavior can be well described by Fickian law and have strong correlation with mechanical properties. The microbond test joint with morphology observation by scanning electron microscope (SEM) and separate evaluation about matrix, fiber and interface unveil that the mechanism of hydrothermal aging of PPS/GF composites and deterioration of interface play the dominant role in degradation of macroscopic mechanical properties. Finally, the life and mechanical properties prediction model of the hydrothermal aged PPS/GF composites were established based on the collected aging data and Arrhenius formula.

Optimization of Rock Mechanical Properties Prediction Model Based on Block Database

Optimization of Rock Mechanical Properties Prediction Model Based on Block Database

Mechanical properties prediction of tire cord steel via multi-stage neural network with time-series data

ABSTRACT Cord steel is a kind of high-quality wire, whose mechanical properties will affect the safety and service life of tire. Therefore, the prediction model of mechanical properties during production process is very important to ensure the quality stability. In the paper, the Multi-Stage Neural Network with Time-Series data (MSNNTS) is proposed to mine the rich information of high-resolution time-series data and represent multistage process to achieve accurate mechanical properties prediction. According to the results, the best mean relative error, for tensile strength prediction, is about 1.25% and the hit rate with 3% error limit is about 98% on the testing set. It also obtains good results in predicting reduction of area. The results show that the method is of great significance to improve the quality stability and uniformity of cord steel.

Mechanical performance degradation of recycled glulam under simulated marine atmosphere

• The physical properties of both specimens change most obviously after the end of the first salt spray stage. • The main failure mode of specimens under compression was not related to the location of the adhesive layer. • The prediction model of mechanical properties of recycled glulam under the influence of corrosion duration is established. • The compression constitutive model and shear constitutive model affected by corrosion duration are proposed. To utilize recycled glulam in coastal constructions, the physical and mechanical properties of original and aged recycled glulam under simulated marine atmosphere were experimentally studied. The results show that the physical properties of the recycled specimens change most obviously after the first salt spray stage (12 days). After salt spray corrosion, the main failure mode of the specimens under compression was oblique shear failure which is not related to the location of the adhesive layer, since no obvious failure happened in the bonding interface. Except for the ductility ratio, the mechanical properties of the recycled glulam decreased with the increase of salt spray corrosion duration (10–20 % and 15–25 % for compressive and shear strength degradation respectively after 48 days), especially the ones after the aging test showing more defects. The compression and shear constitutive models of recycled glulam affected by corrosion duration are proposed and validated by the test results.

Temperature effect on mechanical performance of recycled glulam towards to sustainable production

In order to utilize recycled glulam as basic elements in novel composite structural components towards to sustainable production, the physical and mechanical properties of recycled glulam after exposed to elevated temperatures were experimentally studied, by considering the effect of heating duration (2, 4 and 6 h) and heating temperature (60, 100, 120, 160 and 200 °C). The failure modes and strength of the recycled glulam under compression and shear were analyzed respectively in detail. The results show that the influence of heating temperature on the color change of the specimens is greater than that of heating duration, among which 160 °C is the critical temperature for the obvious color change of glulam. According to the test results, the prediction formulas of weight loss ratio and dimensional change ratio under the influence of heating duration and temperature are established. The compressive strength parallel to the grain of glulam first increases and then decreases, and the shear strength decreases with the increase of heating duration and temperature. The prediction model of mechanical properties of recycled glulam under the influence of heating duration and temperature is established. The decreasing speed of the descending section in the compressive stress-strain curve of glulam increases with the increase of heating duration and temperature. The descending section of the shear stress-strain curve shows two-stage sharp drops due to the obvious brittleness during shear failure. The compression constitutive model and shear constitutive model affected by heating duration and temperature are proposed.

Prediction of Mechanical Properties of Woven Fabrics by ANN

Abstract This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties.

Manipulation of mechanical properties of 7xxx aluminum alloy via a hybrid approach of machine learning and key experiments

Considering the complex relationship among mechanical properties of 7xxx aluminum alloy, it is very crucial to optimize two or more target properties simultaneously in developing new materials. In this paper, three different machine learning assisted strategies are used to study the relationship between alloy composition, process parameters and mechanical properties of 7xxx aluminum alloy, which is expected to accelerate the development of new materials. Firstly, the mechanical properties prediction model of 7xxx aluminum alloy was established by back propagation (BP) neural network. Then, on this basis, genetic algorithm (GA) is used to optimize the prediction accuracy of back propagation (BP) neural network. In addition, a radial basis function (RBF) neural network is used for modeling analysis, and the prediction results of the three models are compared. The results show that back propagation (BP) neural network optimized by genetic algorithm (BP-GA) has higher prediction accuracy than BP and RBF neural network, the coefficient of correlation (R), average absolute relative error (AARE) and root mean squared error (RMSE) value are 0.948, 4.28%, 0.087, respectively. In addition, scanning electron microscope (SEM), electron backscatter diffraction (EBSD), high resolution transmission electron microscope (HRTEM) and the tensile test were used to carry out the related experimental verification work. A large number of fine and dispersed spherical precipitates can be observed after ageing heat treatment. The experimental value is close to the target value, which further confirms that BP neural network model optimized by genetic algorithm can be used to predict and design 7xxx aluminum alloy.

Experimental and analytical study on the mechanical properties of rubberized self-compacting concrete

Rubberized self-compacting concrete (RuSCC) is a newly developed material with the advantages of smaller environmental pollution and improved properties. Validated prediction models of mechanical properties are vital for RuSCC’s application in engineering structures and functional facilities. This paper aims to investigate the influence of rubber aggregates on RuSCC’s mechanical properties by experimental and analytical studies. The experiments explore the mechanical properties of RuSCC with a larger range of rubber ratio than existing studies. The results show that RuSCC with the largest rubber volume ratio of 80% coarse aggregate has the compressive strength, elastic modulus and splitting strength of 14.3%, 25.5% and 16.9% of reference self-compacting concrete (SCC). Increase in RuSCC’s toughness and gradient of post-peak slope is observed from compressive stress-strain curves, indicating higher ductility of RuSCC. Prediction models for RuSCC’s mechanical properties are developed and validated by a database built from reported experiment results. The models show that the decrease of RuSCC’s relative compressive strength is related to the increase of rubber’s ratio and reference SCC’s compressive strength, as well as the replacement of coarse aggregate and the incorporation of supplementary cementitious materials that are not fly ash. Compressive stress-strain constitutive model for RuSCC is developed by modifying SCC’s model and the model predicts the stress-strain curves with good accuracy. The advantages and limitations of proposed model are also discussed following a comparative assessment of its performance on predicting RuSCC’s mechanical properties.

Research on Prediction Models for the Compression-Resilience Performance of Corrugated Metal Gaskets With Residual Stress

Abstract The manufacturing process is an important factor related to the compression-resilience performance of corrugated metal gaskets, especially stainless steel gaskets. A finite element method was employed to research the mechanical properties of corrugated metal gasket based on the stamping and spring back process analysis. On this basis, the influence of structural parameters of corrugated metal gasket related to the mechanical properties was analyzed. Prediction models of mechanical properties were proposed and validated in this paper. The result shows that the maximum contact stress and resilience rate in the finite element model are in accordance with the experimental results with the relative error of 1.21% and 8.76%, respectively. Critical structural parameters affecting the properties of the corrugated metal gasket are pitch (P), lip height (H), and material thickness (T). The compression and resilience curves obtained from the prediction models are consistent with the finite element method (FEM) simulation. The models can accurately reflect the compression-resilience performance of corrugated metal gaskets with different structural parameters.

Experimental investigation and prediction model for mechanical properties of copper-reinforced polylactic acid composites (Cu-PLA) using FDM-based 3D printing technique

Experimental investigation and prediction model for mechanical properties of copper-reinforced polylactic acid composites (Cu-PLA) using FDM-based 3D printing technique

The modeling and mechanical properties prediction of whisker-reinforced ceramic composites

Whiskers have been extensively used in ceramics as excellent reinforcing phases during the past decades. Despite in-depth investigation of whisker-reinforced ceramics (WRC), however, the time-consuming, environmentally unfriendly, content design blindness of experimental method as well as pathogenicity of whiskers force people to explore quick and green research approaches. Therefore, this work aims at developing a modeling approach of WRC and predicting the mechanical properties of WRC, thereby compensating for the disadvantages of traditional experimental method. Initially, a modeling approach of WRC based on Voronoi tessellation was presented. Then, taking β-Si3N4w and Si3N4 as research objects, the prediction models of mechanical properties of 0–6 wt% β-Si3N4w-reinforced Si3N4 ceramics (SWRSC) were established. Finally, the effect of β-Si3N4w content on mechanical properties of Si3N4-based ceramics was discussed, and the enhancement mechanism of β-Si3N4w was revealed. Results indicated that the flexural strength of SWRSC was significantly advanced with increasing β-Si3N4w content. Crack deflection and intergranular fracture induced by β-Si3N4w as well as load-bearing effect of β-Si3N4w were the mainly responsible for the reinforcement of Si3N4-based ceramics. The enhancement mechanisms, previously observed experimentally, were effectively simulated. The fracture toughness reached a maximum of 6.17 MPa m1/2 when β-Si3N4w amount was 1 wt%, which was approximately 36.50% higher than monolithic Si3N4 ceramic. The predicted value of β-Si3N4w optimal content was consistent with relevant experimental conclusions. When β-Si3N4w content was 3 wt%, the hardness of Si3N4-based ceramics reached the maximum value of 23.21 GPa. The effectiveness of prediction results and the presented modeling method were confirmed by comparing with related test values.

Comprehensive investigation and prediction model for mechanical properties of coconut wood–polylactic acid composites filaments for FDM 3D printing

Comprehensive investigation and prediction model for mechanical properties of coconut wood–polylactic acid composites filaments for FDM 3D printing

Construction of a machine-learning-based prediction model for mechanical properties of ultra-fine-grained Fe–C alloy

In recent years, ultra-fine-grained Fe–C alloy have received widespread attention because of their high levels of strength, hardness, and wear resistance. However, these alloy have strict requirements on control of the process parameters during production to meet product specifications. This study was aimed at designing a prediction model for their mechanical properties, which could ultimately promote the industrial development of ultra-fine-grained Fe–C alloy. The effect of process parameters on the mechanical properties of ultra-fine-grained Fe–C alloy was analyzed by electron backscatter diffraction (EBSD) and scanning electron microscope (SEM). By fusing experimental combinations of alloy composition, rolling process, annealing process, and resultant mechanical properties into a dataset for training and verification, a machine-learning-based prediction model of mechanical properties of ultra-fine-grained Fe–C alloy was constructed and tested. When choosing the machine learning algorithm, the best network model was obtained for this application by optimizing a conventional backpropagation (BP) network structure: a three-layer network structure, with a learning rate of 0.5, 18 hidden layer neurons, and activation functions of the hidden and output layers, namely Tansig and Purelin (MATLAB functions), respectively. Finally, the sample data was divided into training set and verification set, and the prediction accuracy, efficiency and generalization ability of BP neural network (BPNN), genetic neural network (GA-BP) and support vector machine (SVM) were compared and analyzed. The superiority of BPNN in the prediction of mechanical properties of ultrafine grained Fe–C alloy was determined, and the mean absolute percentage error (MAPE) of the BPNN for tensile strength, yield strength and uniform elongation are 3.02%, 4.47% and 6.47% respectively, and the number of training steps is 412, which are less than the other two models. The result lays a foundation for the production of high stability and high performance steel products.

Hybrid artificial neural network with biogeography-based optimization to assess the role of cement fineness on ecological footprint and mechanical properties of cement mortar expose to freezing/thawing

The role of cement plants on negative environmental impacts is known. Therefore, investigating the type and amount of cement used in the mix design can significantly reduce the cement consumption and, consequently, reduce the pollution resulting from its excessive production. Also, despite the efforts by researchers to suggest the prediction model of mechanical properties of cementitious materials, there is no reliable model that simultaneously predicts them under freezing and thawing (FT) condition because of the lack of attention to the effective parameters. To fulfil these purposes, initially, an extensive experimental work considering 54 mix designs (810 specimens) was prepared, and the impact of cement fineness in the form of three cement strength classes (CSC 32.5, 42.5 and 52.5 MPa) and amount of cement consumption (Sand/Cement of 2.5, 2.75 and 3.0) on porosity, flexural (Ff) and compressive strength (Fc) under five different FT cycles (0, 50, 100, 150 and 200), and also cement paste texture by SEM and XRD analysis were investigated. Then, a new multi-objective model was developed based on hybrid artificial neural network (ANN) with biogeography-based optimization (BBO) to improve prediction accuracy.Results demonstrated that specimens with fine cement (CSC 52.5 MPa) experienced decreasing the porosity up to 55% and increasing the load carrying capacity about 60% Ff and 80% Fc compared with coarse cement ones (CSC 32.5 MPa) at 200 FT cycles. Also, SEM images and XRD analysis illustrated that the finer cement can improve the homogeneity of the cement paste, and thereby it became denser texture. Comparison of results with previous studies confirmed the S/C of 2.75 as optimum proportion in mix design. Results of sensitivity analysis on models by performance indicators revealed that the best performance for MOANNII-BBO model compared with other models due to considering the CSC and amount of cement consumption as input parameters. Furthermore, the analysis of influencing input parameters indicated the sensitivity of 20% CSC and 18% amount of cement to the model performance. The findings of this work can bring notable benefits for the range of issues involved.