Finite element analysis is widely used to predict the structural stability and tooth contact performance of gears. This study focused on the effect of finite element modeling conditions of a spur gear on the simulation result and the model simplification. The gear body and teeth, teeth width, configuration of mesh, frictional coefficient, and simulation time interval (gear mesh cycle division) were selected for model simplification for gear analysis. The static transmission error during a single-gear mesh cycle was calculated to represent the performance of the gear, and the elapsed time was measured as a simplification factor. Contact stress distribution was also checked. The differences in maximum transmission error and elapsed time depending on the model simplification methods were analyzed. After all simplification methods were estimated, an optimal combination of the methods was defined, and the result was compared with that of the most detailed modeling methods.
This papter presents the use of the automatic differential method based on the backpropagation method to obtain the design sensitivity and its application to topology optimization considering the stress constraints. Solving topology optimization problems with stress constraints is difficult owing to singularities, the local nature of stress constraints, and nonlinearity with respect to design variables. To solve the singularity problem, the stress relaxation technique is used, and p-norm for stress constraints is applied instead of local stresses for global stress measures. To overcome the nonlinearity of the design variables in stress constraint problems, it is important to analytically obtain the exact design sensitivity. In conventional topology optimization, design sensitivity is obtained efficiently and accurately using the adjoint variable method; however, obtaining the design sensitivity analytically and additionally solving the adjoint equation is difficult. To address this problem, the design sensitivity is obtained using a backpropagation technique that is used to determine optimal weights and biases in the artificial neural network, and it is applied to the topology optimization with the stress constraints. The backpropagation technique is used in automatic differentiation and can simplify the calculation of the design sensitivity for the objectives or constraint functions without complicated analytical derivations. In addition, the backpropagation process is more computationally efficient than solving adjoint equations in sensitivity calculations.
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