scholarly journals On the Use of Permutation Tests in the Significance Testing of Response Surface Function Parameters

2020 ◽  
pp. 21-29
Author(s):  
Małgorzata Złotoś
Keyword(s):  
Response Surface ◽  
Permutation Tests ◽  
Significance Testing ◽  
Response Surface Function

scholarly journals Kinematic reliability and sensitivity analysis of the modified Delta parallel mechanism

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141875910 ◽  
Author(s):  
Dongtao Xu
Keyword(s):  
Sensitivity Analysis ◽  
Response Surface ◽  
Parallel Mechanism ◽  
Functional Relation ◽  
Structural Response ◽  
Quadratic Function ◽  
Reliability Sensitivity ◽  
Set Up ◽  
Response Surface Function ◽  
Input Error

In order to improve the kinematic reliability, it is crucial to find out the influence of each error source on the kinematic reliability of the mechanism. Reliability sensitivity analysis is used to find the changing rate in the probability of reliability in relation to the changes in distribution parameters. Based on the structural response surface function method, the functional relation between the kinematic reliability of a modified Delta parallel mechanism and the original input-error vectors is described using the quadratic function with cross terms. Moreover, the partial derivatives of the functional relation with respect to the means and variances of the original input errors are derived, which can efficiently evaluate kinematic reliability sensitivity of the mechanism. The advantages of this method are as follows: First, the response surface function, which can be easily set up by the position-error model of the mechanism, is convenient for calculating the variance, partial derivative, and reliability sensitivity. Second, in this case (unlike in the traditional error-mapping model), although the input-error values are unknown, pseudorandom variables used as random input-error sources can be generated by MATLAB software. Furthermore, the kinematic reliability of the mechanism can be assessed using the Monte Carlo method.

Fuzzy optimization design-based multi-level response surface of bogie frame

2019 ◽  
Vol 10 (2) ◽  
pp. 134-148 ◽  
Author(s):  
Pengpeng Zhi ◽  
Yonghua Li ◽  
Bingzhi Chen ◽  
Meng Li ◽  
Guannan Liu
Keyword(s):  
Boundary Conditions ◽  
Response Surface ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Fuzzy Optimization ◽  
Optimal Solution ◽  
Content Type ◽  
Design Variables ◽  
Multi Level ◽  
Response Surface Function

Purpose In a structural optimization design-based single-level response surface, the number of optimal variables is too much, which not only increases the number of experiment times, but also reduces the fitting accuracy of the response surface. In addition, the uncertainty of the optimal variables and their boundary conditions makes the optimal solution difficult to obtain. The purpose of this paper is to propose a method of fuzzy optimization design-based multi-level response surface to deal with the problem. Design/methodology/approach The main optimal variables are determined by Monte Carlo simulation, and are classified into four levels according to their sensitivity. The linear membership function and the optimal level cut set method are applied to deal with the uncertainties of optimal variables and their boundary conditions, as well as the non-fuzzy processing is carried out. Based on this, the response surface function of the first-level design variables is established based on the design of experiments. A combinatorial optimization algorithm is developed to compute the optimal solution of the response surface function and bring the optimal solution into the calculation of the next level response surface, and so on. The objective value of the fourth-level response surface is an optimal solution under the optimal design variables combination. Findings The results show that the proposed method is superior to the traditional method in computational efficiency and accuracy, and improves 50.7 and 5.3 percent, respectively. Originality/value Most of the previous work on optimization was based on single-level response surface and single optimization algorithm, without considering the uncertainty of design variables. There are very few studies which discuss the optimization efficiency and accuracy of multiple design variables. This research illustrates the importance of uncertainty factors and hierarchical surrogate models for multi-variable optimization design.

Hydrogen desorption properties and electrode performances of Ti–Zr–Ni–V–Mn alloy

1995 ◽  
Vol 10 (7) ◽  
pp. 1680-1686 ◽  
Author(s):  
H.W. Yang ◽  
W.S. Lee ◽  
Y.Y. Wang ◽  
C.C. Wan ◽  
T.W. Cheng ◽  
...  
Keyword(s):  
Factorial Design ◽  
Response Surface ◽  
Electrode Material ◽  
Design Method ◽  
Hydrogen Desorption ◽  
Alloy Structure ◽  
Discharge Rates ◽  
Response Surface Function ◽  
Desorption Capacity

The measurements of the desorption pressure-composition-temperature (P-C-T) of the TixZr1−xNiyV2−y(0 ≤x≤ 1,0 ≤y≤ 2) alloy have been investigated by means of a 32factorial design method. The response surface function of hydrogen desorption between 0.01 and 10 atm was calculated by Yates' algorithm. Alloy withx= 0.35,y= 0.60 (i.e., Ti0.35Zr0.65Ni0.6V1.4) was found to possess maximum hydrogen desorption capacity. When examined by EDAX and SEM, this alloy shows three distinguishable phases and exhibits C14 structure. The effect of substitution of Mn and Ni for V was also studied. Alloy such as Ti0.35Zr0.65Ni1.2V0.4Mn0.4has nearly a pure C14 structure with 89% hydrogen desorption ability. This alloy has 255 mAh/g, 231 mAh/g, and 210 mAh/g capacities at 25 mA/g, 50 mA/g, and 100 mA/g discharge rates, respectively. This indicates that the substitution of Mn and Ni for V not only can improve its hydrogen desorption ability, but also make the alloy structure more uniform and more suitable to be an electrode material.

Uncertainty Updating of Finite Element Models Using Interval Analysis

2020 ◽  
Vol 20 (13) ◽  
pp. 2041012
Author(s):  
Deshan Shan ◽  
Y. H. Chai ◽  
Hao Dong ◽  
Zhonghui Li
Keyword(s):  
Finite Element ◽  
Response Surface ◽  
Interval Analysis ◽  
Surrogate Model ◽  
Structural Parameters ◽  
Natural Extension ◽  
Model Parameters ◽  
Finite Element Models ◽  
Interaction Terms ◽  
Response Surface Function

Uncertainties in structural parameters and measurements can be accounted for by incorporating interval analysis into the updating scheme of finite element models using a response-surface function. To facilitate the interval arithmetic operation, two different strategies are proposed in this paper to transform the response-surface function into a corresponding interval response-surface function. These strategies minimize the inherent interval overestimation that can arise from the variable dependency of the surrogate model. In the first strategy, the natural extension and centered-form extension methods are used to mitigate the interval overestimation of the surrogate model, which may or may not contain interaction terms. In the second strategy, the natural extensión method is also adopted to realize the interval transformation of the surrogate model containing interaction terms but an affine arithmetic is further introduced to minimize the interval overestimation. To demonstrate the efficacy of the proposed method, model parameters are determined from an instrumented model of a cable-stayed bridge tested on a shaking table. Results show that the proposed updating method is feasible and effective for applications to finite element models of complex bridge structures.

scholarly journals Structural Modal Analysis and Optimization of SUV Door Based on Response Surface Method

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hao Chen ◽  
Chihua Lu ◽  
Zhien Liu ◽  
Cunrui Shen ◽  
Yi Sun ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Modal Frequency ◽  
Frequency Matching ◽  
Surface Method ◽  
Design Variables ◽  
Highly Sensitive ◽  
Response Surface Function ◽  
Fifth Order

Sensitivity analysis and response surface methods were employed to optimize the structural modal of SUV doors. A finite element numerical simulation model was established and was calibrated by restraint modal tests. To screen out highly sensitive panels, a sensitivity analysis for the thickness of door panels was proposed based on the fifth-order modal frequency of the door. Data points were obtained by a faced central composite design with the design variables from the thickness of the highly sensitive panels, and a second-order explicit response surface function of the fifth-order modal frequency of the vehicle door was established. An optimization model was established according to the response surface method. The final results demonstrate that the modal-frequency matching of the door and body in white was optimized after changing the thicknesses, with a 5.74% material reduction.

Time-dependent reliability analysis of the motor hanger for EMU based on stochastic process

2019 ◽  
Vol 11 (3) ◽  
pp. 453-469 ◽  
Author(s):  
Pengpeng Zhi ◽  
Yue Xu ◽  
Bingzhi Chen
Keyword(s):  
Stochastic Process ◽  
Reliability Analysis ◽  
Response Surface ◽  
Structural Design ◽  
Time Dependent ◽  
Structural Stress ◽  
Content Type ◽  
Structural Resistance ◽  
Uncertainty Parameters ◽  
Response Surface Function

Purpose Most of the previous work on reliability analysis was based on the traditional reliability theory. The calculated results can only reflect the reliability of components at a specific time, which neglects the uncertainty of load and resistance over time. The purpose of this paper is to develop a time-dependent reliability analysis approach based on stochastic process to deal with the problem and apply it to the structural design of railway vehicle components. Design/methodology/approach First, the parametric model of motor hanger for electric multiple unit (EMU) is established by ANSYS parametric design language, and its structural stress is analyzed according to relevant standards. The Latin hypercube method is used to analyze the sensitivity of the structure, and the uncertainty parameters (sizes and loads) which have great influence on the structural strength are determined. The D-optimal experimental design is carried out to establish the polynomial response surface function, which characterizes the relationship between uncertainty parameters and structural stress. Second, the Poisson stochastic process is adopted to describe the number of loads acting, and the Monte Carlo method is used to obtain the load acting history according to its probability distribution characteristics. The load history is introduced into the response surface function and the uncertainty of other parameters is considered at the same time, and the stress history of the motor hanger is obtained. Finally, the degradation process of structural resistance is described by a Gamma stochastic process, and the time-dependent reliability of the motor hanger is calculated based on the reliability theory. Findings Time and the uncertainties of parameters have great impact on reliability. The results of reliability decrease with time fluctuation are more reasonable, stable and credible than traditional methods. Practical implications In this paper, the proposed method is applied to the structural design of the motor hanger for EMU, which has a good guiding significance for accurately evaluating whether if the design meets the reliability requirements. Originality/value The value of this paper is that the method takes both the randomness of load over time and the uncertainty of structural parameters in the design and manufactures process into consideration, and describes the monotonous degradation characteristics of structural resistance. At the same time, the time-dependent reliability of mechanical components is calculated by a response surface method. It not only improves the accuracy of reliability analysis, but also improves the analysis efficiency and solves the problem that the traditional reliability analysis method can only reflect the static reliability of components.

An Approach to Reliability Analysis Using the Response Surface Method and Monte Carlo Simulation

1999 ◽  
Author(s):  
Irfan Kaymaz ◽  
Chris A. McMahon
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Actual Performance ◽  
Surface Method ◽  
Analysis System ◽  
Different Response ◽  
Response Surface Function

Abstract It is important in reliability evaluation to take an approach in which the required calculations can be performed efficiently in terms of time and cost. In this study, an approach is proposed whereby reliability analysis is carried out by means of Monte Carlo simulation in which the actual performance function is replaced by a function obtained using the response surface method (RSM). The common approach in the conventional RSM is to use a second-degree polynomial for the response surface function, but in many reliability problems this may not be the best choice. This paper first reviews the approaches and limitations of reliability methods, and then goes on to discuss a method of modelling error when using the response surface method for reliability analysis. It shows the errors obtained for different response functions under different circumstances, and then describes the application of a network-based analysis system to reliability problems.

Lightweight Research of Cabin Structure Based on Response Surface Method

2014 ◽  
Vol 711 ◽  
pp. 100-103
Author(s):  
Han Liu ◽  
Fang Zhen Song ◽  
Ming Ming Li ◽  
Bo Song
Keyword(s):  
Response Surface ◽  
Optimization Design ◽  
Design Method ◽  
Test Sample ◽  
Correction Coefficient ◽  
Element Analysis ◽  
The Real ◽  
Response Surface Models ◽  
Surface Models ◽  
Response Surface Function

The selection, fitting and evaluation methods of response surface functions are expounded. The parameter sensitivity analysis of the cabin is carried out. The response surface functions of the stress and the vibration frequencies are constructed through the Box-Behnken experimental design method. Fitting inspection on the response surface functions is done with correlation coefficient, correction coefficient, etc. The results show that the response surface models are very similar to the real models. Four design variables are extracted randomly as the test sample of each response surface function. The data gotten by the response surface function are compared with the data gotten by the finite element analysis. The results show that the response surface models are with high accuracy and can reflect the real test values well. These response surface models can be used for further optimization design. They are helpful in reducing the ship mass without exceeding the allowable stress and resonance.

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