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

Abstract

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.

Multiobjective Intelligent Cooperative Design for the Multilayer Interference Fit

“…Figure 5 shows the optimal solution sets of − f 1 (x) and − f 2 (x). e results revealed that NSGA-II has significant advantages over other algorithms in optimal performances [20]. Specifically, the negative number of maximum equivalent stress and minimum contact pressure from NSGA-II is smaller than that from MOSA and MOPSO.…”

“…Equations (21)∼(25) are solved using NSGA-II, MOSA, and MOPSO algorithm. Among these three algorithms, NSGA-II setting: the population number is 1000, the variation rate is 0.1, and the crossing rate is 0.85; MOSA setting: the Boltzmann coefficient is 1, the maximum internal loop number is 1000, and the cooling coefficient is 0.99; MOPSO setting: the population number is 1000 and the nearest population is 2. e certain type of the shrink disk is designated as an example, and the three algorithms are used to run 20 times independently [20]. e minimum mass obtained by these three algorithms is the same f 3 (x) � 960.970.…”

Hybrid Approach of Finite Element Method, Kigring Metamodel, and Multiobjective Genetic Algorithm for Computational Optimization of a Flexure Elbow Joint for Upper‐Limb Assistive Device

“…The optimization experienced the main steps, such as design a mechanical structure, define design variables and objective functions, build 3D model, evaluate initial performances, and establish the numerical experiments using response surface. These steps can be found in detail by [26].…”

“…This algorithm was proposed for multiobjective optimization problem in this study because it can converge to the global Pareto solutions. This algorithm helped to seek a Pareto-optimal set for multiple objective optimization [26]. The MOGA was a variant of the Nondominated Sorted Genetic Algorithm-II (NSGA-II) that replied on controlled elitism concepts.…”

Soft Sensors in the Primary Aluminum Production Process Based on Neural Networks Using Clustering Methods

“…The success of using these “intelligent” paradigms in modeling dynamic systems is due to the little knowledge required to perform modeling (only a reasonable amount of data is required) compared to other forms of analytical modeling, and also because they are naturally nonlinear models. Among these “intelligent” techniques used for nonlinear dynamic modeling [11,12], one of the most used is artificial neural networks. The use of artificial intelligence in dynamic modeling based on data is sometimes referred to as soft sensors.…”