Design and multi-objective optimization for a broad self-amplified 2-DOF monolithic mechanism

et al. 2019

Self Cite

This paper proposes an effective hybrid optimization algorithm for multiobjective optimization design of a compliant rotary positioning stage for indentation tester. The stage is created with respect to the Beetle’s profile. To meet practical demands of the stage, the geometric parameters are optimized so as to find the best performances. In the present work, the Taguchi method is employed to lay out the number of numerical experiments. Subsequently, the finite element method is built to retrieve the numerical data. The mathematical models are then established based on the response surface method. Before conducting the optimization implementation, the weight factor of each response is calculated exactly. Based on the well-established models, the multiple performances are simultaneously optimized utilizing the teaching learning-based optimization. The results found that the weight factors of safety factor and displacement are 0.5995 (59.95%) and 0.4005 (40.05%), respectively. The results revealed that the optimal safety factor is about 1.558 and the optimal displacement is 2.096 mm. The validations are in good agreement with the predicted results. Sensitivity analysis is carried out to identify the effects of variables on the responses. Using the Wilcoxon’s rank signed test and Friedman test, the effectiveness of the proposed hybrid approach is better than that of other evolutionary algorithms. It ensures a good effectiveness to solve a complex multiobjective optimization problem.

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…For structural optimization, topology, size, or shape optimization is used. Topology permits the connectivity of the domain while the size optimization denotes design [10,[17][18][19][20]. Shape optimization gives a suitable configuration.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Effective Hybrid Algorithm of Taguchi Method, FEM, RSM, and Teaching Learning‐Based Optimization for Multiobjective Optimization Design of a Compliant Rotary Positioning Stage for Nanoindentation Tester

Dang

et al. 2019

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“…Topology represents the connectivity of the domain. Size optimization denotes design parameters such as thickness, cross-sectional area, and length [6][7][8]. Shape optimization represents a suitable configuration.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Approach of Finite Element Method, Artificial Neural Network-Based Multiobjective Genetic Algorithm for Computational Optimization of a Linear Compliant Mechanism of Nanoindentation Tester

Nguyễn

2018

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This paper proposes a new evolutionary multiobjective optimization technique for a linear compliant mechanism of nanoindentation tester. The mechanism design is inspired by the elastic deformation of flexure hinge. To improve overall static performances, a multiobjective optimization design was carried out. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) is developed to solve the optimization problem. In this procedure, the CDD is used to lay out the experimental data. The FEM is developed to retrieve the quality performances. And then, the ANN is developed as black box to call the pseudo-objective functions. Unlike previous studies on multiobjective evolutionary algorithms, most of which generating only one Pareto-optimal solution, this proposed approach can generate more than three Pareto-optimal solutions. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results showed that the optimal results were found at the displacement of 330.68 μm, stress of 140.65 MPa, and safety factor of 3.6. The statistical analysis is conducted to investigate the behavior of the MOGA. The sensitivity analysis was carried out to determine the significant contribution of each factor. The results revealed that the lengths and thickness almost significantly affect both responses. It confirms that the proposed hybrid optimization approach gains high robustness and effectiveness with flexible decision maker rules to solve complex optimization engineering problems.

“…In general, Taguchi method was a robust optimization technique but only used effectively for single objective [31][32][33][34]. For general optimization problems, mathematical models are established, and then an evolutionary optimization algorithm is utilized to find the best solution.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of a Dragonfly-Inspired Compliant Joint for Camera Positioning System of Nanoindentation Tester Based on a Hybrid Integration of Jaya-ANFIS

Nguyễn

2018

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Camera positioning system is a critical member of a nanoindentation tester characterizing the mechanical properties such as hardness, creep, surface roughness, or elastic modulus of a material sample. This paper presents a design optimization for a dragonfly-inspired compliant joint. This joint is used to drive the camera positioning system. A new hybrid approach of Taguchi method, adaptive neuro-fuzzy inference system (ANFIS), and Jaya algorithm is developed to solve the multi-objective optimization problem. The Taguchi method is used to build the numerical data and to find the best membership functions for the ANFIS structure by minimizing the root mean squared error. Then, the weight factor of each objective function is determined by established equations well. Subsequently, a structure of ANFIS is developed to map the design parameters and responses. Sensitivity analysis of each controllable parameter is analyzed by the statistical method. Finally, Jaya algorithm is initialized to find the optimal solution. The results found that the optimal displacement, frequency, and stress are about 12581.11 μm, 67.76 Hz, and 333.68 MPa, respectively. The proposed hybrid optimization algorithm is a robust and effective optimizer and considered as soft computing technique for engineering optimization problems.