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Fractional factorial experiments
Published in Adedeji B. Badiru, Ibidapo-Obe Oye, Babatunde J. Ayeni, Manufacturing and Enterprise, 2018
Adedeji B. Badiru, Ibidapo-Obe Oye, Babatunde J. Ayeni
A three-factor central composite design (Table 11.7) is a member of the most popular class of designs used for estimating the coefficients in the second order model. This design consists of 8 vertices of a 3-dimensional cube. The values of the coded factors in this factorial portion of the design are (B, G, H) = (+1, +1, +1). In addition, this design consists of 6 vertices (+1.63, 0, 0), (0, +1.63, 0), (0, 0, +1.63) of a 3-dimensional octahedron or star and six center points. If properly set up, a central composite design has the ability to possess the constant variance property of a rotatable design or may be an orthogonal design thereby allowing an independent assessment of the three factors under study. For the illustrative study, a second order response surface experiment was conducted for the three factors B, G, and H previously declared statistically significant from the above screening experiment. The design set up is provided in Table 11.10.
Spatial response surface sampling
Published in Mark Stamp, Introduction to Machine Learning with Applications in Information Security, 2023
As with conditioned Latin hypercube sampling, spatial response surface sampling is an experimental design adapted for spatial surveys. Experimental response surface designs aim at finding an optimum of the response within specified ranges of the factors. There are many types of response surface designs, see Myers et al. (2009). With response surface sampling one assumes that some type of low order regression model can be used to accurately approximate the relationship between the study variable and the covariates. A commonly used design is the central composite design. The data obtained with this design are used to fit a multiple linear regression model with quadratic terms, yielding a curved, quadratic surface of the response.
Effect of Operational Uncertainties on the Stochastic Dynamics of Composite Laminates
Published in Sudip Dey, Tanmoy Mukhopadhyay, Sondipon Adhikari, Uncertainty Quantification in Laminated Composites, 2018
Sudip Dey, Tanmoy Mukhopadhyay, Sondipon Adhikari
A central composite design is an experimental design, used in response surface methodology to construct a second order (quadratic) model for the response variable without three-level factorial experiment. The statistical measure of goodness of a model is obtained by least squares regression analysis for the minimum generalized variance of the estimates of the model coefficients. CCD is employed to provide a mathematical and statistical approach in portraying the input-output mapping by construction of meta-model by using the algorithmically obtained sample set (design points). Considering the problem of estimating the coefficients of a linear approximation model by least squares regression analysis (
Effect of process parameters on mechanical properties of mesta (Hibiscus cannabinus) adhesive-bonded nonwoven
Published in The Journal of The Textile Institute, 2022
Surajit Sengupta, Papai Ghosh, Izhar Mustafa
To determine the effects of the factors (variables) on the response parameter, it was decided to use the statistical technique called central composite surface design to develop the design matrix. The matrix so developed was a 20 point central composite design which consists of a full factorial design 23 (8) plus 6 centre points and 6 star-points. All factors at the (0) level constitute the centre points and combination of each factor either at its highest (+1.682) or lowest (–1.682) level with all other factors at the intermediate level constitutes the star points (Cochran & Cox,1963; Montgomery, 2005). The 20 experimental runs, thus, allowed the estimation of the linear, quadratic and two-way interactive effects of the various factors on properties. The design matrix so developed (Sengupta, 2018) with coded values of the factors is given in Table 2.
Comparison of response surface methodology and hybrid-training approach of artificial neural network in modelling the properties of concrete containing steel fibre extracted from waste tyres
Published in Cogent Engineering, 2019
Temitope F. Awolusi, Oluwaseyi L. Oke, Olufunke O. Akinkurolere, Olumoyewa D. Atoyebi
The experimental design often plays a major role in determining the total number of experiments required during the investigation. The study utilized the central composite design of RSM in determining the required number of experiments. According to (Pilkington et al., 2014), the central composite rotatable design provides an opportunity for introducing axial points into the experimental design. A total of 20 experimental runs were generated using the central composite design. The central composite design is generally used to ensure accurate prediction when examining larger spread conditions in which the complexity of the model is not known by providing five levels for each process variable. The architecture for the RSM modelling technique is presented in Figure 4. The process variables considered in the study at different levels are given in both actual and coded terms as follows: Aspect ratio (A): 170(+1.68979), 140(+1),95(0), 50(−1), 19.32(−1.68979)Water-cement ratio (B): 0.45(+1.68979), 0.40(+1),0.33(0),0.25(−1),0.2(−1.68979)Cement content (C): 45.1(+1.68979), 40(+1), 32.5(0),25(−1), 19.(−1.68979)
Research on the contact fatigue failure of thermal sprayed coating based on infrared thermography
Published in Nondestructive Testing and Evaluation, 2020
Runbo Ma, Lihong Dong, Haidou Wang, Wei Guo
Central composite design [21] is a statistical experiment planning method that takes into account the independent and interactive effects of influencing factors. It has been widely used in parameter optimisation design, and the design scheme shall be compiled in coding mode. The coding method is.In the equation, is the coded variable value, is the coding of , n is the number of samples, is the radius of the interval of, andis the centre of the interval of . The method has experimental points, is the number of experimental points of a 2k factor design, is the number of experimental points distributed on m axes (m is the number of axes), and is the number of repetitions of the centre point. The distance from the experimental point to the centre point is the parameter to be determined; properties such as orthogonality and rotation can be obtained by adjusting. In general, if the design is rotatable, it requires. After encoding, the range of the new variable is .