An Optimized Design of New XYθ Mobile Positioning Microrobotic Platform for Polishing Robot Application Using Artificial Neural Network and Teaching-Learning Based Optimization

Abstract

Compliant mechanisms with flexure hinges have been widely applied for positioners, bioengineering, and aerospace. In this study, a new optimized design method for the mobile microrobotic platform was developed for the polishing robot system. A metaheuristic-based machine learning technique in combination with finite element analysis (FEA) was developed. The designed platform allows three degrees of freedom with two x-and-y translations and one z-axis rotation. A new hybrid displacement amplification mechanism was also developed using Scott-Russell and two-lever mechanisms to magnify the workspace of the platform. The leaf hinges were employed due to their large rotation, and the right circular hinges were adopted because of their high accuracy. In modeling the behaviors of the developed platform, the artificial neural network is formulated in combination with the teaching-learning-based optimization (TLBO) method. The ANN architecture was optimized through TLBO to a better approximation. And then, three optimized case studies were conducted by the TLBO. The data is collected through FEA simulation. The modeling results from the TLBO-based ANN were well established with excellent metrics of R, R2, and MSE. The optimized results found that the proposed MPM platform achieves a max-y stroke of 1568.1 μm, max-x stroke of 735.55 μm, and max-θ rotation angle of 2.26 degrees. The proposed MPM platform can operate at a high displacement amplification ratio of over 9.