Prediction of wear strength of squeeze cast aluminium hybrid metal matrix composites using response surface methodology

Srinivasan, Ramachandran; Pridhar, T.; Ramraji, K.; Ramesh, A.

doi:10.1016/j.matpr.2020.03.779

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Let f(η) be an m-order polynomial, so the m+1-order derivative of f(η) is zero. According to equation (21), when m is an odd value, if k ≥(m+1)/2, the truncation error is zero. In comparison, when m is an even value, only k ≥(m+2)/2, the truncation error is zero.…”

Section: Approximate Model Error Analysismentioning

confidence: 99%

“…Although the combination of MCS and finite element analysis can directly deal with the uncertainty of composite materials and is relatively simple, MCS requires a large number of samples and a large amount of calculation, which cannot be accepted by the project. To improve calculation efficiency and reduce calculation costs, some scholars propose to analyze the uncertainty of composite materials by using surrogate models, mainly including response surface methodology (RSM) 9,[18][19][20][21][22][23] , radial basis function (RBF) [24][25][26] and Kriging model. [27][28][29][30][31][32] Kumar 23 established a 2D finite element model based on higher-order zigzag theory and studied the failure probability of composite/sandwich shells using MCS and RSM.…”

Section: Introductionmentioning

confidence: 99%

“…Nakayama 20 also established an RSM-based surrogate model, and investigated the probabilistic strength of composite pin joints considering the uncertainty of size and strength parameters. Srinivasan 21 built an RSM-based surrogate model for the wear strength of aluminum hybrid metal matrix composites, and optimized the manufacturing process parameters that were considered as uncertain. Prakash 22 set up an RSM-based surface roughness model of composite panels by using spindle speed, feed rate and drilling diameters as input variables, and studied the influence of drilling parameters on surface roughness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A dimension-reduction based Chebyshev polynomial method for uncertainty analysis in composite corrugated sandwich structures

Zhao

et al. 2022

Journal of Composite Materials

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A prediction method for the ultimate load of composite corrugated sandwich structures (CCSS) was established based on the dimension-reduction method and Chebyshev polynomial. First, an ultimate load response function of the CCSS was reduced in dimension on basis of the univariate method. The original response function was converted to the sum of univariate functions of all random variables. Chebyshev polynomial was used to fit all univariate functions, and the univariate Chebyshev approximate model (UCAM) of the response function was obtained. Second, the fitting accuracy of the UCAM was analytically compared with the Kriging model, radial basis function, and response surface methodology. Finally, the effect of the order on the fitting accuracy of UCAM was studied. The UCAM has higher fitting accuracy and calculation efficiency compared with the other three approximate models. When the order of UCAM is lower than ninth-order, the larger the order, the higher the fitting accuracy. And the order and the fitting accuracy are no longer positively correlated when the order exceeds ninth-order. The order is recommended to choose an odd-order when using UCAM to solve the uncertainty problem.

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Section: Approximate Model Error Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A dimension-reduction based Chebyshev polynomial method for uncertainty analysis in composite corrugated sandwich structures

Zhao

et al. 2022

Journal of Composite Materials

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“…The reliability of the RSM has been verified through experiments. Srinivasan et al [ 7 ] investigated the prediction of the wear strength of metal matrix composites using the RSM, and a good agreement between the experimental and predicted value was found. He et al [ 8 ] established a response surface quadratic model for a carbon fiber composite hull and studied its strength and stability.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Li,

Zhu

2024

Polymers

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A novel method is proposed to quickly predict the tensile strength of carbon/epoxy composites with resin-missing defects. The univariate Chebyshev prediction model (UCPM) was developed using the dimension reduction method and Chebyshev polynomials. To enhance the computational efficiency and reduce the manual modeling workload, a parameterization script for the finite element model was established using Python during the model construction process. To validate the model, specimens with different defect sizes were prepared using the vacuum assistant resin infusion (VARI) process, the mechanical properties of the specimens were tested, and the model predictions were analyzed in comparison with the experimental results. Additionally, the impact of the order (second–ninth) on the predictive accuracy of the UCPM was examined, and the performance of the model was evaluated using statistical errors. The results demonstrate that the prediction model has a high prediction accuracy, with a maximum prediction error of 5.20% compared to the experimental results. A low order resulted in underfitting, while increasing the order can improve the prediction accuracy of the UCPM. However, if the order is too high, overfitting may occur, leading to a decrease in the prediction accuracy.

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“…The effect of the addition of C content on the wear behavior and wear mechanism of the NiCoCrMoCu alloy under different conditions and different parameters was analyzed. We used the response surface analysis (RSM) [ 36 , 37 ] method to analyze the experimental results, which can enumerate the correlation between input parameters and output variables with the minimum number of experiments and is very suitable for establishing the relationship between wear behavior and parameter variables in wear experiments.…”

Section: Introductionmentioning

confidence: 99%

Tribocorrosion Behavior of NiCoCrMoCu Alloys Containing Different Carbides in Acidic Media at Various Applied Loads and Sliding Speeds

Li,

Zeng,

Teng

et al. 2024

Materials

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In this study, the ball-on-disk sliding wear and tribocorrosion behavior in the H2SO4 and HCl solution of NiCoCrMoCu alloys with carbon additions of 0.2, 1, 1.5, and 2 wt.% with the Al2O3 ball as a counterpart was investigated systematically. Obvious tribocorrosion antagonistic effects were found after wear in both aqueous solutions. Compared with dry sliding wear conditions, the lubrication effect of the aqueous solution significantly reduces the wear rate of the alloy, and the reduction effect in the H2SO4 aqueous solution was more obvious than that in HCl. The antagonistic effects of the 0.2C and 1C alloys decrease with the load and sliding rate, while those of the 1.5C and 2C alloys increase. The (coefficient of friction) COF and wear rate under different loads and sliding rates were analyzed using the response surface analysis (RSM) method. It was found that the COF mainly showed dependence on the sliding rate, while the wear rate showed dependence on load and sliding speed.

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Prediction of wear strength of squeeze cast aluminium hybrid metal matrix composites using response surface methodology

Cited by 12 publications

References 12 publications

A dimension-reduction based Chebyshev polynomial method for uncertainty analysis in composite corrugated sandwich structures

A dimension-reduction based Chebyshev polynomial method for uncertainty analysis in composite corrugated sandwich structures

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Tribocorrosion Behavior of NiCoCrMoCu Alloys Containing Different Carbides in Acidic Media at Various Applied Loads and Sliding Speeds

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