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By A K Chitale; R C Gupta

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Taylor series approximation), but adequately evaluates the robustness of the design problems which include correlated fluctuant factors or nonlinear objective functions to which conventional method cannot be applicable. In this paper, it is concluded that the robustness index is calculated using the Monte Carlo method because of the rapidly improvement of computing machine performance and increased user needs for safe and reliable products in recent years. Future tasks is to apply this index to the design problems and have already started[4,5].

The beam will propagate inside the cone following the total internal reflection principle. The incident angle with respect to the side wall of a cone is θn=900(2n-3)α, n=2,3,···,where (n-1) is the number of crossing points between a beam and side wall, and α is the semiapex angle of a cone. This expression indicates that θn will gradually decrease with the increase of n until it is less than critical angle Ω=arcsin(1/ξ) , and the light is refracted out of the side wall of a cone, where ξ is the refractive index of the cone.

Kinematic accuracy U11 Transmission accurac y U 12 Te chnical indicators U1 Me chanical stiffness U13 Adjustable performance U 14 Environmenta l suitability U 15 Dimensional compactness U 16 Design cost U21 Ma nufactur ability U22 Evaluation indicator tree Te chnical indicators Economic indicator s U2 Working life U 23 Ser viceability U 24 Structural complexity U 25 Transmission efficiency U 26 Safety U31 Noise and shock U 32 Social indicators U3 Maneuverability U33 Renewability U 34 Fig. 1 Evaluation Objective Tree of Mechanical Concept Multilevel Evaluation Model Based on Neural Network The neural network based evaluation model is trained using available data sets coming from successful engineering cases, called training samples, through a specific algorithm.

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