scholarly journals Coupled Interval Genetic Algorithm Technique with the Finite Element Method for the Interval Optimization of Structures

2009 ◽  
Vol 3 (5) ◽  
pp. 694-703 ◽  
Author(s):  
Ting-Nung SHIAU ◽  
Chung-Hao KANG ◽  
De-Shin LIU ◽  
Wei-Chun HSU
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Interval Optimization ◽  
The Finite Element Method ◽  
Element Method

Optimization Research on Workpiece Clamping Deformation Using Genetic Algorithm and Finite Element Method

2011 ◽  
Vol 189-193 ◽  
pp. 2153-2160
Author(s):  
Yu Wen Sun ◽  
Chuan Tai Zhang ◽  
Qiang Guo
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Machining Accuracy ◽  
Clamping Force ◽  
The Finite Element Method ◽  
Linear Programming Method ◽  
Fixture Layout ◽  
The Stability ◽  
Element Method

Optimal fixture involves fixture layout and clamping force determination. It is critical to ensure the machining accuracy of workpiece. In this paper, the clamping process is analyzed with the consideration of cutting forces and frictions using the finite element method. Then the fixture layout and clamping force are optimized by minimizing the workpiece deformation via a Genetic Algorithm (GA). Subsequently, linear programming method is used to estimate the stability of workpiece. It is shown through an example that the proposed method is proved to be efficient. The optimization result is not only far superior to the experiential one, but also the total optimization time can be reduced significantly.

Coevolutionary and genetic algorithm based building spatial and structural design

2015 ◽  
Vol 29 (4) ◽  
pp. 351-370 ◽  
Author(s):  
Hèrm Hofmeyer ◽  
Juan Manuel Davila Delgado
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Design Process ◽  
Process Simulation ◽  
Structural Design ◽  
The Finite Element Method ◽  
Design Modification ◽  
Spatial Design ◽  
Element Method

AbstractIn this article, two methods to develop and optimize accompanying building spatial and structural designs are compared. The first, a coevolutionary method, applies deterministic procedures, inspired by realistic design processes, to cyclically add a suitable structural design to the input of a spatial design, evaluate and improve the structural design via the finite element method and topology optimization, adjust the spatial design according to the improved structural design, and modify the spatial design such that the initial spatial requirements are fulfilled. The second method uses a genetic algorithm that works on a population of accompanying building spatial and structural designs, using the finite element method for evaluation. If specific performance indicators and spatial requirements are used (i.e., total strain energy, spatial volume, and number of spaces), both methods provide optimized building designs; however, the coevolutionary method yields even better designs in a faster and more direct manner, whereas the genetic algorithm based method provides more design variants. Both methods show that collaborative design, for example, via design modification in one domain (here spatial) to optimize the design in another domain (here structural) can be as effective as monodisciplinary optimization; however, it may need adjustments to avoid the designs becoming progressively unrealistic. Designers are informed of the merits and disadvantages of design process simulation and design instance exploration, whereas scientists learn from a first fully operational and automated method for design process simulation, which is verified with a genetic algorithm and subject to future improvements and extensions in the community.

scholarly journals Prediction and optimization of thinning in automotive sealing cover using Genetic Algorithm

2015 ◽  
Vol 3 (1) ◽  
pp. 63-70 ◽  
Author(s):  
Ganesh M. Kakandikar ◽  
Vilas M. Nandedkar
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Process Parameters ◽  
Deep Drawing ◽  
Drawing Process ◽  
The Finite Element Method ◽  
Deep Drawing Process ◽  
Compressive Stresses ◽  
Element Method

Abstract Deep drawing is a forming process in which a blank of sheet metal is radially drawn into a forming die by the mechanical action of a punch and converted to required shape. Deep drawing involves complex material flow conditions and force distributions. Radial drawing stresses and tangential compressive stresses are induced in flange region due to the material retention property. These compressive stresses result in wrinkling phenomenon in flange region. Normally blank holder is applied for restricting wrinkles. Tensile stresses in radial direction initiate thinning in the wall region of cup. The thinning results into cracking or fracture. The finite element method is widely applied worldwide to simulate the deep drawing process. For real-life simulations of deep drawing process an accurate numerical model, as well as an accurate description of material behavior and contact conditions, is necessary. The finite element method is a powerful tool to predict material thinning deformations before prototypes are made. The proposed innovative methodology combines two techniques for prediction and optimization of thinning in automotive sealing cover. Taguchi design of experiments and analysis of variance has been applied to analyze the influencing process parameters on Thinning. Mathematical relations have been developed to correlate input process parameters and Thinning. Optimization problem has been formulated for thinning and Genetic Algorithm has been applied for optimization. Experimental validation of results proves the applicability of newly proposed approach. The optimized component when manufactured is observed to be safe, no thinning or fracture is observed.

An integrated finite element method, response surface methodology, and evolutionary techniques for modeling and optimization of machining fixture layout for 3D hollow workpiece geometry

2016 ◽  
Vol 231 (23) ◽  
pp. 4344-4359 ◽  
Author(s):  
KA Sundararaman ◽  
KP Padmanaban ◽  
M Sabareeswaran ◽  
S Guharaja
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Quadratic Model ◽  
Machining Accuracy ◽  
The Finite Element Method ◽  
Fixture Layout ◽  
Element Method

Machining fixtures play inevitable role in manufacturing to ensure the machining accuracy and workpiece quality. The layout of fixture elements, clamping forces, and machining forces significantly affect the workpiece elastic deformation during machining. The clamping and machining forces are necessary to immobilize and machine the workpiece, respectively. Finding the appropriate layout of fixture elements is the other possible way to reduce the workpiece deformation, which in turn improves the machining accuracy. The finite element method interfaced with evolutionary techniques is normally used for fixture layout optimization. In the finite element method, the workpiece is discretized into a number of small elements and fixture elements are placed only on the nodes. Hence, evolutionary techniques are capable of searching the optimal fixture layout from those discrete nodal points than from the entire area on the locating and clamping face. To overcome these limitations, in this research paper, response surface methodology is employed to establish a quadratic model between the position of fixture elements and maximum workpiece deformation. This enables the optimization techniques to search for the optimal solution in the continuous domain of the solution space. Then, the real-coded genetic algorithm based discrete optimization, continuous optimization based on binary-coded genetic algorithm and particle swarm optimization are employed to optimize the developed quadratic model and their performances are compared. The result clearly shows that the integration of finite element method, response surface methodology with particle swarm optimization is better than the integration with genetic algorithm to optimize the machining fixture layout and also reduces the computational complexity and time to a greater extent.

Prediction and Design of the Optimal Punch Shape for Recess Forging

2006 ◽  
Vol 532-533 ◽  
pp. 440-443 ◽  
Author(s):  
Chun Yin Wu ◽  
Yuan Chuan Hsu ◽  
Tung Sheng Yang
Keyword(s):  
Genetic Algorithm ◽  
Finite Element Method ◽  
Finite Element ◽  
Optimal Design ◽  
The Finite Element Method ◽  
Modeling Analysis ◽  
Network Prediction ◽  
Forging Die ◽  
Punch Shape ◽  
Element Method

In this study, the finite element method was used to analyze comprehensively the effects of punch shape on forming the forging recess. Then, the polynomial network and genetic algorithm were combined to construct the predicted and designed system. Through this approach, we can predict the forging results formed from arbitrary shaped punches, and design the optimal punch shape for forging recess. Through the interactive verifies of modeling repeatedly, the errors resulted from modeling analysis, network prediction and genetic algorithm optimal design are extremely limited. Consequently, the predicted and designed approach of optimal punched shape for forming recess in this study could be extended to the design of more complicated and difficult formed forging die.

Simulation of tuning graphene plasmonic behaviors by ferroelectric domains for self-driven infrared photodetector applications

Nanoscale ◽  
2019 ◽  
Vol 11 (43) ◽  
pp. 20868-20875 ◽  
Author(s):  
Junxiong Guo ◽  
Yu Liu ◽  
Yuan Lin ◽  
Yu Tian ◽  
Jinxing Zhang ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Interfacial Effect ◽  
Infrared Photodetector ◽  
The Finite Element Method ◽  
Ferroelectric Domains ◽  
Element Method

We propose a graphene plasmonic infrared photodetector tuned by ferroelectric domains and investigate the interfacial effect using the finite element method.

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