A Hybrid Computational Method of Desirability, Fuzzy Logic, ANFIS, and LAPO Algorithm for Multiobjective Optimization Design of Scott Russell Compliant Mechanism

Abstract

Flexure-based compliant mechanisms are increasingly promising in precision engineering, robotics, and other applications, thanks to the excellent advantages of no friction, no backlash, no wear, and minimal assembly. However, their design and analysis are still challenging due to the coupling of kinematic-mechanical behaviors with large nonlinear deflections in comparison to their rigid-body counterparts. Optimal design is an important aspect in the field of compliant mechanisms and has attracted much attention during the last decades. Especially, when considering a multiobjective optimization design for compliant mechanisms, the problem is becoming more complicated. Thus, this article presents a new efficient hybrid computational method to resolve multiobjective optimization design of compliant mechanisms. A Scott Russell compliant mechanism is employed as the design example and to show the advantages of the proposed optimizing method. The proposed method is developed by hybridizing the desirability function approach, fuzzy logic system, adaptive neuro-fuzzy inference system (ANFIS), and lightning attachment procedure optimization (LAPO). First of all, a 3D finite element model is created and central composite design is employed to build a numerical matrix. First, design variables are refined by using analysis of variance and Taguchi approach in terms of considerably eliminating space of design variables. Subsequently, desirability values of two objective functions are determined and transferred into the fuzzy logic system. The output of the fuzzy logic system is considered as a single combined objective function. Next, modeling for fuzzy output is established via developing the ANFIS model. At last, the LAPO algorithm is adopted for solving the multiobjective optimization problem for the mechanism. Three numerical examples are investigated to validate the feasibility and the effectiveness of performance efficiency of the proposed methodology. The results find that the proposed methodology is more efficient than traditional Taguchi-based fuzzy logic. Besides, the performance efficiency of the proposed approach outperforms the Jaya algorithm and TLBO algorithm through the Wilcoxon signed rank test and Friedman test. The results of this article give a useful approach for complex optimization problems.

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

“…Simply put, in the light of FFISs, so many applications can be considered in which applying FFIS yields to more satisfactory results than ever before. The fields in which FFIS can prove applicable include fault detection [17], control systems [18], [19], handoff decision algorithms [20], internet of thing [21], risk assessment [22], [23], evaluating the quality of experience [24], decision support systems [25], fuzzy clustering and classification [26], [27], fuzzy image processing [28], fuel cell stack problem [29], educational systems [30], fuzzy modelling [31], psychology [32], emotion categories [33], packet scheduling algorithms [34], multiobjective optimization problem [35], decision-making [36], heuristic algorithms [37], etc.…”

Optimal shape selection of pseudo-rigid-body model compliant centrifugal clutch manufactured by thermoplastics using TOPSIS approach

“…Tran et al 10 created a novel hybrid computational approach for solving Scott Russell-compliant mechanisms by combining the desirability function methodology, LAPO algorithm, fuzzy logic, and artificial neuro-fuzzy models. Results show that the suggested hybrid methodology’s prediction accuracy is better than conventional Taguchi-based fuzzy logic.…”

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

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