A Multi-response Optimal Design of Bridge Amplification Mechanism Based on Efficient Approach of Desirability, Fuzzy Logic, ANFIS and LAPO Algorithm

Compliant mechanism is becoming increasingly to be a part of precision engineering, robotics, and bioengineering thanks to excellent advantages of free friction, free lubricant, no backlash, monolithic structure, and minimal assembly. However, design and analysis of compliant mechanism has been facing challenges due to a coupling of kinematic and mechanical behaviors in comparison with rigid-body counterparts. Particularly, considering a multi-response optimization design, the problem is becoming more and more complex. Thus, this article proposes a new efficient hybrid methodology to resolve multi-objective optimization design of compliant mechanisms. A bridge amplification mechanism with three numerical examples is a case study to demonstrate the effectiveness of the proposed optimizing technique. A hybridization is developed by combining finite element method, statistical method, desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system (ANFIS), and lightning attachment procedure optimization (LAPO). A 3D finite element model for the bridge amplification mechanism is designed, and then Box–Behnken design is employed to construct numerical experiments. The sensitivity of geometrical parameters of the mechanism is investigated through analysis of variance and Taguchi approach to make a few populations for the LAPO. Subsequently, desirability values of the displacement and safety factor of the mechanism are determined, and the results are transferred into the fuzzy logic system. The output of this system is considered as single combined objective function. By developing the ANFIS structure, the refined design variables are well mapped with the output of FIS. Finally, LAPO algorithm is adopted for solving the multi-objective optimization problem for the mechanism. The results reveal that the proposed method is more efficient than the Taguchi-based fuzzy logic. Besides, the performances of the proposed method are superior to the Jaya algorithm and TLBO algorithm through Wilcoxon signed rank test and Friedman test. The results of this article can be useful for complex optimization problems.