Application of Grey Relational Approach and Artificial Neural Network to Optimise Design Parameters of Bridge-Type Compliant Mechanism Flexure Hinge

  • Huynh Ngoc Thai Faculty of Automotive Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
  • Nguyen Quoc Manh Faculty of Mechanical Engineering, Hung Yen University of Technology and Education, Hung Yen, Vietnam
Keywords: Bridge-type mechanism; Displacement amplification ratio; Grey relational approach; Artificial neural network; Flexure hinge

Abstract

The investigation proposed a hybrid Grey-artificial neural network to optimise the design parameters of a two degree of freedom (2-DOF) bridge-type compliant mechanism flexure hinge (BTCMFH). The design variables play a vital role in determining the deformation and stress of the mechanism. The investigation is different from the previous studies where the hybrid method is a combination of grey relational analysis and artificial neural network based on finite element method (FEM) in ANSYS to maximise output displacement (DI) and minimise the stress (ST) of the mechanism. The simulation and ANOVA results identified the design variables have significantly affected the output displacement and stress by their contribution. The grey relational analysis and artificial neural network predicted values are in agreement with the simulation results at optimal combination parameters with a deviation error displacement and stress being 0.57% and 2.1%, respectively. The optimal combination parameters with a deviation error of displacement and stress of 0.52% and 2.1%, respectively. The optimal values of DI and ST were obtained as 0.957 mm and 104.74 MPa, respectively. The optimal value of displacement amplification ratio gained is 95.7.

Published
2021-03-02
How to Cite
Thai, H., & Manh, N. (2021). Application of Grey Relational Approach and Artificial Neural Network to Optimise Design Parameters of Bridge-Type Compliant Mechanism Flexure Hinge. International Journal of Automotive and Mechanical Engineering, 18(1), 8505 -. https://doi.org/10.15282/ijame.18.1.2021.10.0645
Section
Articles