Robust Synthesis of Function Mechanisms by Using Asymmetric Quality Loss Models

In order to account for asymmetric quality loss problems resulted from quality characteristics deviation to targets, we propose asymmetric loss models. Firstly, truncated normal distribution theory is applied to computing the expected value for asymmetric loss functions. Symmetric and asymmetric optimization models are then built for robust synthesis of a slider-crank mechanism. Compared with symmetric quality loss model, asymmetric quality loss model has the advantage to shift away from big quality loss and reduce quality loss. Finally, the Monte Carlo simulation solutions show that the proposed approach is effective in robust design for problems with asymmetric quality losses.