Abstract
To make the optimal design of the multilink transmission mechanism applied in mechanical press, the intelligent optimization techniques are explored in this paper. A preference polyhedron model and new domination relationships evaluation methodology are proposed for the purpose of reaching balance among kinematic performance, dynamic performance, and other performances of the multilink transmission mechanism during the conceptual design phase. Based on the traditional evaluation index of single target of multicriteria design optimization, the robust metrics of the mechanism system and preference metrics of decision-maker are taken into consideration in this preference polyhedron model and reflected by geometrical characteristic of the model. At last, two optimized multilink transmission mechanisms are designed based on the proposed preference polyhedron model with different evolutionary algorithms, and the result verifies the validity of the proposed optimization method.
Highlights
To improve work efficiency of mechanical press and acquire specific kinematic and dynamic output of slider, multilink transmission mechanisms are applied to replace the traditional crank-link mechanisms
This research focuses on the optimal design problems of the multilink transmission mechanism in the conceptual design phase
Considering the conflicting objectives, as well as the highly complex search space and constraints, a rigorous and quantitative multidisciplinary design methodology and evaluation standard of design scheme are needed for solving such multiobjective optimization problem (MOOP)
Summary
To improve work efficiency of mechanical press and acquire specific kinematic and dynamic output of slider, multilink transmission mechanisms are applied to replace the traditional crank-link mechanisms. For this reason, this research focuses on the optimal design problems of the multilink transmission mechanism in the conceptual design phase. The evolutionary algorithms (EAs) could provide efficient solutions to the above problems [1, 2]. In such optimization problems, the design objectives describe all the product functions as well as the constraints under which these functions should be realized. The feasibility of applying evolutionary algorithms to the solutions of multiobjective engineering optimization problems has been explored by many previous researches.
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