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Optimization for a Flexure Hinge Using an Effective Hybrid Approach of Fuzzy Logic and Moth-Flame Optimization Algorithm


Minh Phung Dang, Hieu Giang Le, Ngoc Le Chau, Thanh-Phong Dao

Source title: 
Mathematical Problems in Engineering, 2021: 6622655, 2021 (ISI)
Academic year of acceptance: 

Flexure hinge is a critical element in a positioner of a nanoindentation tester. To effectively work, a suitable flexure hinge should simultaneously meet multiple objectives, including rotation axis shift, safety factor, and angular deflection. The main aim of this article was to illustrate a hybrid method of the Taguchi method, fuzzy logic, response surface method, and Moth-flame optimization algorithm to solve the design optimization of a flexure hinge in order to enhance the three quality characteristics of the flexure hinge. Firstly, four common flexure hinges are compared together to seek the best suitable one. Secondly, numerical experiments are gathered via the Taguchi-based detasFlex software. Thirdly, three objective functions are transferred into signal to noises in order to eliminate the unit differences. Later on, fuzzy modeling is proposed to interpolate these three objective functions into one integrated objective function. An integrated regression equation is built using the response surface method. Finally, the flexure hinge is optimized by the Moth-flame optimization algorithm. The results found that the rotation axis shift is 10.944∗10−5 mm, the high safety factor is 2.993, and the angular deflection is 52.0058∗10−3 rad. The verifications are in a suitable agreement with the forecasted results. An analysis of variance and sensitivity analysis are also performed to identify the effects and meaningful contributions of input variables on the integrated objective function. In addition, employing the Wilcoxon signed rank test and the Friedman test, the results find that the proficiency of the proposed method has more benefits than the ASO algorithm and the GA. The results of this research provide a beneficial approach for conducting complicated multiobjective optimal problems.