University of Tehran Press Journal of Computational Applied Mechanics 2423-6713 54 2 2023 06 01 Optimum design of a micro-positioning compliant ‎mechanism based ‎on neural network ‎metamodeling 236 253 91015 10.22059/jcamech.2023.351454.775 EN Erfan Norouzi Farahani School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran. Niloofar Ramroodi School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran. Maryam Mahnama School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran. Journal Article 2022 11 22 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.‎ https://jcamech.ut.ac.ir/article_91015_b880d521049a0a3feb02b08fec337def.pdf