A general approach is presented for generating pin-jointed compliant mechanisms considering geometrical nonlinearity. An optimization problem is formulated for minimizing the total structural volume under constraints on the displacement at the specified node, and stiffnesses at initial and final states, where the design variables are cross-sectional areas and the nodal coordinates. It is shown in the numerical examples that several mechanisms can be naturally found as a result of optimization starting from randomly selected initial solutions. The effect of extensional and compressional stiffnesses on the optimal solutions are also discussed. It is also shown that no local bifurcation point exist along the equilibrium path, and the obtained mechanism is not sensitive to intial imperfections.

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A robust truss optimization scheme, as well as an optimization algorithm, is presented based on the robustness function. Under the uncertainties of external forces modeled by using the info-gap decision theory, we formulate the maximization problem of the robustness function. A sequential semidefinite programming method is proposed which has the global convergent property. It is shown, in numerical examples, that optimum designs of various trusses can be found without any difficulty.

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A design methodology to cope with uncertain fluctuations of structural parameters is presented in line with convex model of uncertainty. The fluctuations are confined within a hyper-ellipse, in which the worst case for structural integrity is identified. The size of hyper-ellipse means range of uncertainty, then it is employed as uncertainty index. A robustness is the maximum horizon of uncertainty index such that the worst case never exceeds critical state. A design to maximize the robustness is searched for by means of conventional optimization technique. A design example concerning with six-bar truss structure under uncertain loads is demonstrated to verify the presented methodology.

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In the present paper, we applied a new optimization method using a stochastic process to the design of die hang glider. As for this problem, not only the hang glider design but also its flight path are optimized at the same time. The method obtains a solution as an expectation (probability average) using stochastic process. The advantages of this method are not to be affected by initial conditions and not to need techniques based on experience. In this optimization problem, the numerical calculation results show that the method has a sufficient performance.

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In our previous studies, we proposed approximate optimization system using RBF network as approximation tool and Genetic Range Genetic Algorithms as optimizer, we showed that it provides necessary data efficiently regardless of whether constrained or unconstrained and we can get high-precision approximation in small number of function call. Although, it is often pointed out that RBF network is effective in approximating complicated response, but inadequate to approximate simple response, especially one has linearity. We will propose approximation that combine RBF network and linear regression or polynomial regression, and use it in our optimization system. In this method, we make the regression model to get rough tendency of the response, and RBF network learns the difference between the response and the regression model. When the response has strong linearity, the regression model fits the response and RBF network value become near to zero, thereby we will get good approximation of the response. In addition, we can predict the response that is in no RBF bases region and it will work well to find next searching points. We will use proposed method to benchmark problems and show the effectiveness of proposed method

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It has been clearly shown that a can or PET bottle made by origami technique can be crushed with much smaller force. Furthermore, this structure is desirable to absorb more crash energy during automobile light collision or vehicles-pedestrian accidents. Thus, in this research, to minimize average crash force of the origami structure, the shape of the structure and the angle of polygonal folding lines are optimized by the method of MPOD, one kind of Response Surface Methods based on Holographic Neural Network (HNN).

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In this paper, we introduce a new approach to surface mesh improvement problem. In contrast to previous methods we do not tend to preserve new mesh vertices on the original discrete surface. Instead our technique keeps mesh nodes very close to a smooth or piecewise-smooth surface approximated by an initial mesh. As a result, the algorithm is able to improve mesh quality while preserving essential surface characteristics and features. Proposed approach can be applied iteratively not only to polygonal meshes but also to 2D and 3D curves that allows to treat sharp edges and surface boundaries. We demonstrate Effectiveness of our method using various triangular and quadrilateral meshes.

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