This paper develops a hybrid optimization approach for multi-criteria optimal design of a compliant positioning platform for nanoindentation tester. The platform mimics the biomechanical behavior of beetle so as to allow a linear motion. Structure of the beetle-liked mechanism consists of six legs arranging in a symmetric topology. Amplification ratio and static characteristics of the platform are analyzed by finite element analysis (FEA). To improve the performances of the platform, the main geometric parameters of the platform are optimized by an efficient hybrid approach of the Taguchi method (TM), response surface methodology (RSM), improved adaptive neuro-fuzzy inference system (ANFIS), and teaching learning based optimization (TLBO). Numerical data are collected by integrating of the RSM and FEA. Signal to noise ratios are determined and the weight factor of each response is calculated. The suitable ANFIS’s parameters are optimized through the TM. The results found that trapezoidal-shaped MFs is the best type for the safety factor and the displacement. The optimal ANFIS’s parameters for the safety factor and the displacement were determined at the number of input MFs of 4, trapmf, hybrid learning method, and linear output MFs. According to improved ANFIS establishments, TLBO algorithm is utilized for solving the multi-objective optimization. Analysis of variance and sensitivity are investigated to determine the significant effects of design factors on the responses. The simulated and experimental validations are in a good agreement with the predicted results.
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The authors are thankful for the financial support from the HCMC University of Technology and Education, Vietnam, under Grant No. T2019-05TD.
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Dang, M.P., Le, H.G., Le Chau, N. et al. A multi-objective optimization design for a new linear compliant mechanism. Optim Eng 21, 673–705 (2020). https://doi.org/10.1007/s11081-019-09469-8
- Compliant positioning platform
- Multi-objective optimization
- Taguchi method
- Response surface method
- Adaptive neuro-fuzzy inference system
- Weight factor