The sample data used to train neural networks were schemed according to design of experiment theory, the application of two kinds of neural networks-back propagation network and radial basic function network were investigated in detail, and a new optimization method was proposed.

A three-dimensional optimization design system of torque converter cascade is developed, including meanline initial value searching, torus and vanes parameterization, grid generation, CFD analysis, design of experiment and optimization algorithm, and for each segment a corresponding design tool is presented.

A three-dimensional optimization design system of torque converter cascade is developed, including meanline initial value searching, torus and vanes parameterization, grid generation, CFD analysis, design of experiment and optimization algorithm, and for each segment a corresponding design tool is presented.

RBF neural networks were adopted to construct the response relation between the design variable and the objective function, and the training sample data were schemed according to the design of experiment theory. As there was many design variables and the design space was very large, a new subset based approach was proposed.

Optimization of Atmospheric Plasma Spray Process Parameters using a Design of Experiment for Alloy 625 coatings

Grit blast media, blast pressure, and working distance were varied using a Box-type statistical design of experiment (SDE) approach.

A statistical design of experiment approach was used and the results were analyzed using both the linear regression method and average response of factors calculations.

A design of experiment study of plasma-sprayed alumina-titania coatings

These nanostructures are synthesized by an electrospinning technique wherein the process parameters are varied through a design of experiment (DoE) methodology.

A concept of robustness into the optimal specification of Flexible Manufacturing Systems (FMS) is introduced and an effective method to realize the robust design is presented. The Response Surface Model of FMS is drafted combining the design of experiments, simulation experiments, and analysis of data by this method, The robust and optimal specification of FMS is realized through optimizing the model. The proposed approach is also demonstrated by determining the robust and optimal specification...

A concept of robustness into the optimal specification of Flexible Manufacturing Systems (FMS) is introduced and an effective method to realize the robust design is presented. The Response Surface Model of FMS is drafted combining the design of experiments, simulation experiments, and analysis of data by this method, The robust and optimal specification of FMS is realized through optimizing the model. The proposed approach is also demonstrated by determining the robust and optimal specification of an engineering instance. And the result shows that the method not only can realize the robust design of FMS, but also is general to the initial design of other complicated systems.

The ordinary parameterization programs of three-dimensional blade contain a great deal of design variables, therefore an indirect parameterization method was proposed, where the angular momentum was treated as design variables, the blade was calculated by inverse design method, and neural networks were adopted to construct the response relation between the design variable and the objective function. The sample data used to train neural networks were schemed according to design of experiment theory, the...

The ordinary parameterization programs of three-dimensional blade contain a great deal of design variables, therefore an indirect parameterization method was proposed, where the angular momentum was treated as design variables, the blade was calculated by inverse design method, and neural networks were adopted to construct the response relation between the design variable and the objective function. The sample data used to train neural networks were schemed according to design of experiment theory, the application of two kinds of neural networks-back propagation network and radial basic function network were investigated in detail, and a new optimization method was proposed. Compared with the ordinary optimization programs, fewer variables were required in this method based on the three-dimensional viscous computational fluid dynamics(CFD) analysis and the calculation time was shortened obviously. An optimized blade in a mixed-flow pump, where the head and the efficiency were selected as the objective functions, confirms the validity of this newly proposed method.

The blade in a mixed-flow pump was optimized based on three dimensional viscous flow analysis and neural networks with the blade angle being treated as design variable and the efficiency being treated as objective function. RBF neural networks were adopted to construct the response relation between the design variable and the objective function, and the training sample data were schemed according to the design of experiment theory. As there was many design variables and the design space was...

The blade in a mixed-flow pump was optimized based on three dimensional viscous flow analysis and neural networks with the blade angle being treated as design variable and the efficiency being treated as objective function. RBF neural networks were adopted to construct the response relation between the design variable and the objective function, and the training sample data were schemed according to the design of experiment theory. As there was many design variables and the design space was very large, a new subset based approach was proposed. Firstly, the design variables were divided into several groups, and a subset was formed in each group. Then, in each subset, part of the blade was optimized respectively and independently. Finally, the whole blade was optimized with the effect of each subset being taken into account. An optimized blade in a mixed-flow pump, where te efficiency was highly improved, confirms the validity of this newly proposed method.