scholarly journals Some Studies on Forming Optimization with Genetic Algorithm

2012 ◽  
Vol 2 (2) ◽  
pp. 105-112
Author(s):  
Ganesh Marotrao KAKANDIKAR ◽  
Vilas M. NANDEDKAR
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Wide Spectrum ◽  
Simulation Software ◽  
Flow Conditions ◽  
Element Analysis ◽  
Blank Holder ◽  
New Approach ◽  
Finite Element Analysis Simulation ◽  
Selection Of

Forming is a compression-tension process involving wide spectrum of operations and flow conditions. The result of the process depends on the large number of parameters and their interdependence. The selection of various parameters is still based on trial and error methods. In this paper the authors presents a new approach to optimize the geometry parameters of circular components, process parameters such as blank holder pressure and coefficient of friction etc. The optimization problem has been formulated with the objective of optimizing the maximum forming load required in Forming. Genetic algorithm is used for the optimization purpose to minimize the drawing load and to optimize the process parameters. A finite element analysis simulation software Fast Form Advanced is used for the validations of the results after optimization.

A new approach for optimum selection of a turbocharger using a genetic algorithm

2014 ◽  
Vol 229 (8) ◽  
pp. 1016-1033 ◽  
Author(s):  
Sepehr Sanaye ◽  
Shahram Sedghi Ghadikolaee ◽  
Mohammad Mehdi Ghasemi ◽  
Golandam Rahimi
Keyword(s):  
Genetic Algorithm ◽  
New Approach ◽  
Optimum Selection ◽  
Selection Of

Application of FEM Simulation and Abductive Network to Predict the Springback of U-Shaped Bending Process with Counter Force

2012 ◽  
Vol 579 ◽  
pp. 32-41
Author(s):  
Tung Sheng Yang ◽  
Jen Chuan Yeh ◽  
Sheng Yi Chang
Keyword(s):  
Finite Element ◽  
Process Parameters ◽  
Fem Simulation ◽  
Data Sets ◽  
The Finite Element Method ◽  
Blank Holder Force ◽  
Element Analysis ◽  
Blank Holder ◽  
Bending Process ◽  
Abductive Network

This study applies the finite element method (FEM) in con-junction with an abductive network to predict springback’s angle during the U-shaped bending process with counter force. To verify the prediction of FEM simulation for springback, the experimental data are compared with the results of current simulation. Bending force, effective stress distribution and springback are investigated for different process parameters, such as profile radius of die, blank holder force and counter force of U-shaped bending process, by finite element analysis. The abductive network is then utilized to synthesize the data sets obtained from numerical simulations. Finally, prediction model is established for predicting springback’s angle under a suitable range of process parameters.

scholarly journals An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Ramin Ranjbarzadeh ◽  
Amir Hussein Dadkhah ◽  
Yaghoub Pourasad ◽  
Malika Bendechache
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Method ◽  
Fitness Function ◽  
Jaccard Index ◽  
Diabetic Patients ◽  
Growth Region ◽  
New Approach ◽  
Fuzzy C Means ◽  
Extended Approach ◽  
Selection Of

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

Collaborative Optimization for the Cold Extrusion Die and Process Parameters of Wheel Hub Bearing Rings

2010 ◽  
Vol 34-35 ◽  
pp. 13-18
Author(s):  
Bin Meng ◽  
Hai Wei Wu ◽  
Guan Jun Bao ◽  
Qing Hua Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Process Parameters ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Cold Extrusion ◽  
Element Analysis ◽  
Extrusion Die

The traditional optimization method for cold extrusion forming needs to perform finite element analysis repeatedly and therefore has to consume significant computational resource. This paper describes a collaborative optimization method for the cold extrusion die and process parameters of wheel hub bearing rings, combined using finite element analysis, orthogonal experiment, neural network and genetic algorithm. Orthogonal experiment is used to design experimental schemes. Neural network is used to establish mapping relationship between die and process parameters and maximum extrusion force. Genetic algorithm is used to optimize cold extrusion die and process parameters. Via this approach the finite element analysis is relatively independent of optimization process, which just provides training samples of neural network and evaluates the optimized results obtained by genetic algorithm. It overcomes the deficiency of large computational resource consumption of traditional optimization method and provides a fast and effective approach for die and process optimization of cold extrusion forming.

A Study on Warm Hydroforming of Al and Mg Sheet Materials: Mechanism and Proper Temperature Conditions

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Ho Choi ◽  
Muammer Koç ◽  
Jun Ni
Keyword(s):  
Process Parameters ◽  
Control Process ◽  
Hydraulic Pressure ◽  
Element Analysis ◽  
Lightweight Materials ◽  
Blank Holder ◽  
Warm Hydroforming ◽  
Temperature Levels ◽  
Fully Coupled

Hydroforming of lightweight materials at elevated temperature is a relatively new process with promises of increased formability at low internal pressure levels. In this study, the mechanism of warm hydroforming processes is presented in terms of its formability by comparison with warm forming, and cold hydroforming processes. Additionally, a strategy is proposed to control process parameters, such as temperature, hydraulic pressure, blank holder force, and forming speed. As a part of this strategy, the proper temperature condition is determined by adaptive-isothermal finite element analysis (FEA) and a design of experiment (DOE) approach. The adaptive-isothermal FEA determines the temperature levels of the blank material, which is selectively heated, by checking position of the blank material and adopting temperature level of the neighboring tooling. The proposed adaptive-isothermal FEA/DOE approach leads to the optimal temperature condition in a warm hydroforming system accurately and rapidly as opposed to costly and lengthy experimental trial and errors and/or fully coupled thermo-mechanical simulations. Other process parameters are also optimized in a continued study (Choi et al., 2007, “Determination of Optimal Loading Profiles in Warm Hydroforming of Lightweight Materials,” J. Mater. Process. Techn., 190(1–3), pp. 230–242.).

scholarly journals Modeling and Finite Element Analysis Simulation of MEMS Based Acetone Vapor Sensor for Noninvasive Screening of Diabetes

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
John Ojur Dennis ◽  
Almur Abdelkreem Saeed Rabih ◽  
Mohd Haris Md Khir ◽  
Mawahib Gafare Abdalrahman Ahmed ◽  
Abdelazez Yousif Ahmed
Keyword(s):  
Critical Concentration ◽  
Simulation Software ◽  
Vapor Concentration ◽  
Exhaled Breath ◽  
Acetone Vapor ◽  
Sensing Element ◽  
Element Analysis ◽  
Flexible Beams ◽  
Finite Element Analysis Simulation ◽  
Cmos Mems

Diabetes is currently screened invasively by measuring glucose concentration in blood, which is inconvenient. This paper reports a study on modeling and simulation of a CMOS-MEMS sensor for noninvasive screening of diabetes via detection of acetone vapor in exhaled breath (EB). The sensor has two structures: movable (rotor) and fixed (stator) plates. The rotor plate is suspended on top of the stator by support of four flexible beams and maintaining certain selected initial gaps of 5, 6, 7, 8, 9, 10, or 11 μm to form actuation and sensing parallel plate capacitors. A chitosan polymer of varied thicknesses (1–20 μm) is deposited on the rotor plate and modeled as a sensing element for the acetone vapor. The minimum polymer coating thickness required to detect the critical concentration (1.8 ppm) of acetone vapor in the EB of diabetic subjects is found to be 4–7 μm, depending on the initial gap between the rotor and stator plates. However, to achieve sub-ppm detection limit to sense the acetone vapor concentration (0.4–1.1 ppm) in the EB of healthy people, up to 20 μm polymer thickness is coated. The mathematically modeled results were verified using the 2008 CoventorWare simulation software and a good agreement within a 5.3% error was found between the modeled and the simulated frequencies giving more confidence in the predicted results.

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