Optimum design of a micro-positioning compliant ‎mechanism based ‎on neural network ‎metamodeling

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Norouzi Farahani Erfan; Ramroodi Niloofar; Mahnama Maryam

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Journal Paper

Paper Information

Journal: THE JOURNAL OF COMPUTATIONAL APPLIED MECHANICS Year:621 | Volume: | Issue: Page(s): 236-253

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Information Journal Paper

Title

Optimum design of a micro-positioning compliant ‎mechanism based ‎on neural network ‎metamodeling

Author(s)

Norouzi Farahani Erfan | Ramroodi Niloofar | Mahnama Maryam | Issue Writer Certificate

Keywords

Compliant mechanismQ4

Finite Element Analysis (FEA)Q4

MetamodelQ4

Deep Neural Networks ‎‎(DNN)Q4

‎‎Single-Objective Constrained Particle Swarm Optimization (SOCPSO) algorithmQ4

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.‎

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