Modeling, analysis and multi-objective optimization of twist extrusion process using predictive models and meta-heuristic approaches, based on finite element results

2014 ◽  
Vol 27 (2) ◽  
pp. 463-473 ◽  
Author(s):  
Hamed Bakhtiari ◽  
Mahdi Karimi ◽  
Sina Rezazadeh
Keyword(s):  
Finite Element ◽  
Predictive Models ◽  
Extrusion Process ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Heuristic Approaches ◽  
Modeling Analysis ◽  
Twist Extrusion

The Efficient Multi-Objective Optimization of Finite Element Analysis Model Using ModelCenter

2016 ◽  
Author(s):  
Lyu Wang ◽  
Yuan Yun ◽  
Bin Zhang ◽  
Tao Zhang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Frequency Response ◽  
Space Environment ◽  
Multi Objective Optimization ◽  
Analysis Model ◽  
Element Analysis ◽  
Finite Element Analysis Model ◽  
Multi Objective ◽  
Software Analysis

The multi-objective optimization for a nested flying vehicle (NFV) of space science experiments is carried out aiming at the launch weight, frequency response and vacuum effect. The parametric model and finite element analysis are adopted to implement the structural analysis. The NFV is optimized to enhance the performance in the space environment where the lunch weight and structural strength are key constraints to concern about. The CAX software, analysis models and algorithms are integrated based on ModelCenter framework which makes modeling, analyzing and optimization more convenient and efficient. The optimizer of ModelCenter is chosen to optimize the structural performance of NFV, including the total mass, maximum deformation caused by vacuum environment and frequency response. As to validate the results, both weighting method with gradient optimization algorithm and Genetic Algorithm (GA) for multi-objective optimization are used. The optimization results of NFV verify the approaches proposed in this paper can improve the performance of NFV and apply to the finite element analysis model.

The Multi-Objective Optimization of the Planet Carrier in Wind Turbine Gearbox

2012 ◽  
Vol 184-185 ◽  
pp. 565-569 ◽  
Author(s):  
Peng Xing Yi ◽  
Li Jian Dong ◽  
Yuan Xin Chen
Keyword(s):  
Finite Element ◽  
Design Optimization ◽  
Response Surface ◽  
Minimum Principle ◽  
Element Model ◽  
Multi Objective Optimization ◽  
Wind Turbine Gearbox ◽  
Element Analysis ◽  
Multi Objective ◽  
Planet Carrier

In order to improve the reliability of a planet carrier, a simulation method based on multi-objective design optimization was developed in this paper. The objective of the method was to reduce the stress concentration, the deformation, and the quality of the planet carrier by optimizing the structure dimension. A parametric finite element model, which enables a good understanding of how the parameters affect the reliability of planet carrier, was established and simulated by ANSYS-WORKBENCH. The efficiency of the design optimization was improved by using a polynomials response surface to approximate the results of finite element analysis and a screening algorithm to determine the direction of optimization. Furthermore, the multi-objective optimization was capable of finding the global minimum results in the use of the minimum principle on the response surface. Computer simulation was carried out to verify the validity of the presented optimization method, by which the quality and the stability of the planet carrier were significantly reduced and improved, respectively. The methodology described in this paper can be effectively used to improve the reliability of planet carrier.

The Multiobjective Design Optimization of pMUT Hydrophone

2015 ◽  
Vol 727-728 ◽  
pp. 660-665
Author(s):  
Shun Hsyung Chang ◽  
Fu Tai Wang ◽  
Jiing Kae Wu ◽  
Sergey N. Shevtsov ◽  
Igor V. Zhilyaev ◽  
...  
Keyword(s):  
Finite Element ◽  
Frequency Response ◽  
Design Optimization ◽  
Frequency Band ◽  
Vacuum Chamber ◽  
Fe Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Membrane Type ◽  
Multiobjective Design

The paper presents some results of multi-objective optimization for the multilayered membrane-type piezoceramic MEMS based transducers with perforated active PZT and intermediate diaphragms, covered by the protective plates, and a vacuum chamber. An influence of the protective plate elastic and viscous properties, the dimensions and the relative areas of the perforated holes on the sensitivity’s frequency response of the hydrophone was studied for the broadening and equalizes the operating frequency band. We optimize the key design’s parameters using the Pareto approach with the finite element (FE) model of coupled piezoelectric-acoustic problem for the hydrophone.

Mixture Surrogate Models for Multi- Objective Optimization

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Shailesh S. Kadre ◽  
Vipin K. Tripathi
Keyword(s):  
Finite Element ◽  
Optimization Problems ◽  
Surrogate Models ◽  
Optimization Techniques ◽  
Optimization Process ◽  
Multiple Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Costly Function ◽  
Selection Of

Multi-objective optimization problems (MOOP) involve minimization of more than one objective functions and all of them are to be simultaneously minimized. The solution of these problems involves a large number of iterations. The multi- objective optimization problems related structural optimization of complex engineering structures is usually solved with finite element analysis (FEA). The solution time required to solve these FEA based solutions are very high. So surrogate models or meta- models are used to approximate the finite element solution during the optimization process. These surrogate assisted multi- objective optimization techniques are very commonly used in the current literature. These optimization techniques use evolutionary algorithm and it is very difficult to guarantee the convergence of the final solution, especially in the cases where the budget of costly function evaluations is low. In such cases, it is required to increase the efficiency of surrogate models in terms of accuracy and total efforts required to find the final solutions.In this paper, an advanced surrogate assisted multi- objective optimization algorithm (ASMO) is developed. This algorithm can handle linear, equality and non- linear constraints and can be applied to both benchmark and engineering application problems. This algorithm does not require any prior knowledge for the selection of surrogate models. During the optimization process, best single and mixture surrogate models are automatically selected. The advanced surrogate models are created by MATSuMoTo, the MATLAB based tool box. These mixture models are built by Dempster- Shafer theory (DST). This theory has a capacity to handle multiple model characteristics for the selection of best models. By adopting this strategy, it is ensured that most accurate surrogate models are selected. There can be different kind of surrogate models for objective and constraint functions. Multi-objective optimization of machine tool spindle is studied as the test problem for this algorithm and it is observed that the proposed strategy is able to find the non- dominated solutions with minimum number of costly function evaluations. The developed method can be applied to other benchmark and engineering applications.

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