Optimum design of a micro-positioning compliant mechanism based on neural network metamodeling
Document Type : Research Paper
Authors
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Abstract
This paper presents a comprehensive investigation of the optimization process of a compliant nano-positioning mechanism based on a high-accuracy metamodel. Within this study, analytical approach, finite element analysis (FEA), and deep neural network (DNN) are integrated in order to achieve the optimum design of a parallel 2-degree-of-freedom compliant positioner while taking a broad range of factors into account. First, a linear regression analysis is performed on the primary finite element model as a sensitivity analysis. Then an analytical model is established to express one of the objective functions of design, namely the mechanism working range, as a function of characteristic features: the mechanism stiffness and displacement amplification ratio (λ). In the optimization procedure, a single objective constrained particle swarm optimization (SOCPSO) algorithm acts on the metamodel to maximize the resonant frequency and provide the minimum acceptable working range. The proposed optimization guideline is established for seven different desired working ranges and succeeded in predicting the objective function with an error of less than 3%. The findings provide insights into the design and geometric optimization of the mechanical structures. Furthermore, it will be employed as a guideline for implementing DNN for metamodeling in other engineering problems.
Keywords
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Volume 54, Issue 2
June 2023Pages 236-253
- Receive Date: 22 November 2022
- Revise Date: 10 February 2023
- Accept Date: 11 February 2023