scholarly journals 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

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ngoc Le Chau ◽  
Thanh-Phong Dao ◽  
Van Thanh Tien Nguyen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Compliant Mechanism ◽  
Hybrid Optimization ◽  
Pareto Optimal ◽  
Optimization Approach ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm

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.

scholarly journals OPTIMIZATION OF MULTISPAN RIBBED PLYWOOD PLATE MACROSTRUCTURE FOR MULTIPLE LOAD CASES

2013 ◽  
Vol 19 (5) ◽  
pp. 696-704 ◽  
Author(s):  
Jānis Šliseris ◽  
Kārlis Rocēns
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Multilayer Plate ◽  
Maximal Stress ◽  
Optimization Methodology ◽  
Stress Criteria ◽  
Multiple Load

This paper discusses an optimized structural plate of plywood composite that consists of top and bottom plywood flanges and a core of plywood ribs. The objective function is structure's weight. Typical constrains – maximal stress criteria and maximal deformation criteria – are used. The optimization is done by Genetic Algorithm (GA), and optimization results are used to train Feed-Forward Artificial Neural Network. The numerical simulation of plywood structure is done by using classical linear Kirchoff–Love theory of multilayer plate and Finite Element Method. As a result, an effective optimization methodology for plywood composite material is proposed. The most rational (according to strength-stiffness criteria) plywood composite macrostructure is obtained for some typical cases.

scholarly journals 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

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Duc Nam Nguyen ◽  
Thanh-Phong Dao ◽  
Ngoc Le Chau ◽  
Van Anh Dang
Keyword(s):  
Elbow Joint ◽  
Strain Energy ◽  
Hybrid Approach ◽  
Hybrid Optimization ◽  
Assistive Device ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multiobjective Genetic Algorithm ◽  
Design Variables

Modeling for robotic joints is actually complex and may lead to wrong Pareto-optimal solutions. Hence, this paper develops a new hybrid approach for multiobjective optimization design of a flexure elbow joint. The joint is designed for the upper-limb assistive device for physically disable people. The optimization problem considers three design variables and two objective functions. An efficient hybrid optimization approach of central composite design (CDD), finite element method (FEM), Kigring metamodel, and multiobjective genetic algorithm (MOGA) is developed. The CDD is used to establish the number of numerical experiments. The FEM is developed to retrieve the strain energy and the reaction torque of joint. And then, the Kigring metamodel is used as a black-box to find the pseudoobjective functions. Based on pseudoobjective functions, the MOGA is applied to find the optimal solutions. Traditionally, an evolutionary optimization algorithm can only find one Pareto front. However, the proposed approach can generate 6 Pareto-optimal solutions, as near optimal candidates, which provides a good decision-maker. Based on the user’s real-work problem, one of the best optimal solutions is chosen. The results found that the optimal strain energy is about 0.0033 mJ and the optimal torque is approximately 588.94 Nm. Analysis of variance is performed to identify the significant contribution of design variables. The sensitivity analysis is then carried out to determine the effect degree of each parameter on the responses. The predictions are in a good agreement with validations. It confirms that the proposed hybrid optimization approach has an effectiveness to solve for complex optimization problems.

scholarly journals Analyzing student performance using evolutionary artificial neural network algorithm

2018 ◽  
Vol 7 (2.26) ◽  
pp. 67 ◽  
Author(s):  
A S. Arunachalam ◽  
T Velmurugan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Student Performance ◽  
Fitness Function ◽  
Research Work ◽  
Function Evaluation ◽  
Success Factor ◽  
Optimization Approach ◽  
Artificial Neural

Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clarifying beneficial understanding from educational databases for many purposes such as predicting student’s success factor. The ability to predict a student’s performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more precisely.

Combined optimization of a linear PM actuator with trapezoidal PMs and coils considering fringing effect for precision engineering

2021 ◽  
pp. 1-21
Author(s):  
Jun-Wei Chen
Keyword(s):  
Magnetic Field ◽  
Genetic Algorithm ◽  
Finite Element Method ◽  
Fourier Series ◽  
Fourier Series Expansion ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
The Finite Element Method ◽  
Precision Engineering

For precision engineering, a linear PM-moving actuator with trapezoidal PMs and trapezoidal coils considering the fringing effect is proposed. To take into account the effect of the finite-long trapezoidal PM array, an improved Fourier series expansion is developed to calculate the fringing-included magnetic field. Then the full-stroke thrust excited by the trapezoidal coils is accurately predicted and validated by the finite element method. Sensitivity of the trapezoidal parameters of PMs and coils is analyzed, and combined optimization is implemented by the genetic algorithm. Through the Pareto optimal solutions of thrust, the relation of the PM-coil parameter combination is described and formulated by curve fitting. Compared with the traditional rectangular PM actuators or other trapezoidal-typed actuators, the proposed actuator with trapezoidal PMs and coils further decreases the thrust ripple and largely increases the thrust magnitude simultaneously, and reaches an utmost-close effective stroke as well.

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