To achieve high work performance for compliant mechanisms of motion scope, continuous work condition, and high frequency, we propose a new hybrid algorithm that could be applied to multi-objective optimum design. In this investigation, we use the tools of finite element analysis (FEA) for a magnification mechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements. A poly-algorithm including the Grey-Taguchi method, fuzzy logic system, and adaptive neuro-fuzzy inference system (ANFIS) algorithm, was utilized mainly in this study. The FEA outcomes indicated that design variables have significantly affected on magnification ratio of the mechanism and verified by analysis of variance and analysis of the signal to noise of grey relational grade. The results are also predicted by employing the tool of ANFIS in MATLAB. In conclusion, the optimal findings obtained: Its magnification is larger than 40 times in comparison with the initial design, the maximum principal stress is 127.89 MPa, and the first modal shape frequency obtained 397.45 Hz. Moreover, we found that the outcomes obtained deviation error compared with predicted results of displacement, stress, and frequency are 8.76%, 3.6%, and 6.92%, respectively.

Productivity and quality are always two goals in the production process. In metal cutting, two prominent representatives of quality and productivity are roughness and material removal rate (MRR). In this study, the Response Surface method was used to perform single-objective and multiobjective optimizations during the hard milling of SKD11 steel. From there, comparative analyzes are carried out to give effective advice for different approaches in actual production. The selected inputs are the nanoparticle concentration in the cutting oil and three typical cutting parameters including cutting velocity, depth of cut, and feed rate. Each input will have three levels including low, high and average. The L27 orthogonal array developed by Taguchi was applied to the experimental design. In addition, ANOVA was also used to evaluate the statistical indicators of the study. The results of single-objective optimization show that the feed rate is the main influencing factor for the roughness followed by the nanoparticle concentration. They contribute 51.2 and 21.12 of the total roughness effect, respectively. On the other hand, the main factors affecting the material removal rate are the depth of cut and feed rate. In multiobjective optimization, a compromise solution has also been proposed to achieve small roughness and high material removal rate. The minimum roughness was 0.1956μm and the maximum material removal rate was 1479.8688mm³/min when applying the multiobjective optimal machining condition.

The steering system in the car assumes the task of navigating when the car is in motion. It not only plays a technical role but also performs a crucial role in vehicle safety. In this study, the model of a steering system with helical gear and incline rack was created by Solidworks. And the maximum principal stress, and maximum principal strain of helical gear and rack were determined by finite element analysis in ANSYS. The data of simulation were used to minimize the stress and the strain by the Taguchi method based on grey relational analysis. The results of finite element analysis indicated that input variables have significantly affected on stress and strain of gear and rack. And then the problems are verified by analysis of the signal to noise, analysis of variance, and regression analysis. All are in good agreement with the error of the predicted value and optimal value of GRG is 0.022%. The optimal results of the stress and strain of gear and rack achieved 0.1638 MPa and 0.0188 MPa, 7.676 x10-7 mm and 3.5687x10-7 mm, respectively.