Process Optimal Design in Forging by Genetic Algorithm

2002 ◽  
Vol 124 (2) ◽  
pp. 397-408 ◽  
Author(s):  
J. S. Chung ◽  
S. M. Hwang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Process Design ◽  
Process Model ◽  
Hot Forging ◽  
Objective Functions ◽  
Optimal Design Problem ◽  
Design Problems ◽  
Design Iteration ◽  
Design Variables

A genetic algorithm based approach is presented for process optimal design in forging. In this approach, the optimal design problem is formulated on the basis of the integrated thermo-mechanical finite element process model so as to cover diverse design variables and objective functions, and a genetic algorithm is adopted for conducting design iteration for optimization. The process model, the formulation for process optimal design, and the genetic algorithm are described in detail. The approach is applied to several selected process design problems in cold and hot forging.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

Optimal Design of Composite Wing-Box by Genetic Algorithm

2014 ◽  
Vol 697 ◽  
pp. 365-368
Author(s):  
Guang Rong Pu ◽  
Peng Gang Mu
Keyword(s):  
Genetic Algorithm ◽  
Minimum Weight ◽  
Design Problems ◽  
Finite Element Program ◽  
Structural Layout ◽  
Design Variables ◽  
Load Factors ◽  
Element Program ◽  
Composite Wing ◽  
Wing Box

With the increasing use of composite materials in aviation structures, stability and weights of wing-box are important projects that engineers care about. In this paper, the genetic algorithm is chosen to deal with the conceptual design problems of composite wing-box. For the more excellent capabilities in optimization computation of multi-dimensional functions, particularly when overcoming local-best solutions, genetic algorithm is presented to determine the design variables of complicated wing-box. Optimization algorithm is realized with MATLAB software, which calls the finite element program MSC.Nastran to get buckling load factors, and structural layout, thickness of plies and minimum weight of wing-box are obtained simultaneously. The results show that the approach proposed is available, effective to preliminary design of the mainly aeronautical structures.

Remarks on Sufficiency of Constraint-Bound Solutions in Optimal Design

1988 ◽  
Author(s):  
P. Y. Papalambros
Keyword(s):  
Nonlinear Programming ◽  
Optimal Design ◽  
Design Problems ◽  
Local Optima ◽  
Solution Strategies ◽  
Active Constraints ◽  
Active Sets ◽  
Trade Offs ◽  
Design Variables ◽  
Monotonicity Analysis

Abstract Solution strategies for optimal design problems in nonlinear programming formulations may require verification of optimality for constraint-bound points. These points are candidate solutions where the number of active constraints is equal to the number of design variables. Models leading to such solutions will typically offer little insight to design trade-offs and it would be desirable to identify them early, or exclude them in a strategy using active sets. Potential constrained-bound solutions are usually identified based on the principles of monotonicity analysis. This article discusses some cases where these points are in fact global or local optima.

scholarly journals Multi-Objective Optimisation of an Aerostatic Pad: Design of Position, Number and Diameter of the Supply Holes

2020 ◽  
Vol 36 (3) ◽  
pp. 347-360
Author(s):  
F. Colombo ◽  
F. Della Santa ◽  
S. Pieraccini
Keyword(s):  
Genetic Algorithm ◽  
Load Capacity ◽  
Design Parameters ◽  
Objective Functions ◽  
Aerostatic Bearing ◽  
Multiobjective Optimisation ◽  
Damping Coefficients ◽  
Supply Pressure ◽  
Design Variables ◽  
Stiffness And Damping

ABSTRACTIn this paper, a rectangular aerostatic bearing with multiple supply holes is optimised with a multiobjective optimisation approach. The design variables taken into account are the supply holes position, their number and diameter, the supply pressure, while the objective functions are the load capacity, the air consumption and the stiffness and damping coefficients. A genetic algorithm is applied in order to find the Pareto set of solutions. The novelty with respect to other optimisations which can be found in literature is that number and location of the supply holes is completely free and not associated to a pre-defined scheme. A vector x associated with the supply holes location is introduced in the design parameters and given in input to the optimizer.

Multidisciplinary Design Exploration for Sounding Launch Vehicle using Hybrid Rocket Engine in View of Ballistic Performance

2015 ◽  
Vol 32 (3) ◽  
Author(s):  
Kazuhisa Chiba ◽  
Masahiro Kanazaki ◽  
Atthaphon Ariyarit ◽  
Hideyuki Yoda ◽  
Shoma Ito ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Rocket Engine ◽  
Launch Vehicle ◽  
Test Design ◽  
Objective Functions ◽  
Hybrid Rocket ◽  
Ballistic Performance ◽  
Design Problems ◽  
Design Variables ◽  
Hybrid Rocket Engine

AbstractConceptual design of a launch vehicle with a hybrid rocket engine (HRE) has been implemented using design informatics approach in order to investigate the feasibility of a single-stage hybrid rocket. Two test design problems were formulated by using two objective functions: maximization of downrange and minimization of initial gross weight, seven design variables which describe geometry and initial conditions, and one constraint relative to target altitude. The optimization result reveals the economical performance of hybrid rocket is limited with HRE in terms of the maximum downrange achievable. Moreover, the data-mining result indicates the mechanism of design-variable behavior.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM

2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Steel Structures ◽  
American Concrete Institute ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Pareto Fronts

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.

Optimal Design of Traction Drive Continuously Variable Transmissions

1989 ◽  
Vol 111 (2) ◽  
pp. 264-269 ◽  
Author(s):  
K. H. Lim ◽  
D. G. Ullman
Keyword(s):  
Optimal Design ◽  
Power Loss ◽  
Problem Formulation ◽  
Programming Algorithm ◽  
Design Technique ◽  
Optimal Design Problem ◽  
Design Problems ◽  
Traction Drive ◽  
Constraint Equations ◽  
Continuously Variable Transmissions

An optimal design technique for minimum power loss in traction drive continuously variable transmissions is developed. The general forms of the objective function and constraint equations are derived, and the formulated optimal design problems are implemented in a nonlinear programming algorithm. Kinematic analysis and optimal design problem formulation are performed for a selected traction drive configuration as an example of the procedures.

Geometric Size Optimization of Annular Step Fin Using Multi-Objective Genetic Algorithm

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Abhijit Deka ◽  
Dilip Datta
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Cross Sectional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

Although an annular stepped fin can produce better cooling effect in comparison to an annular disk fin, it is yet to be studied in detail. In the present work, one-dimensional heat transfer in a two-stepped rectangular cross-sectional annular fin with constant base temperature and variable thermal conductivity is modeled as a multi-objective optimization problem. Taking cross-sectional half-thicknesses and outer radii of the two fin steps as design variables, an attempt is made to obtain the efficient fin geometry primarily by simultaneously maximizing the heat transfer rate and minimizing the fin volume. For further assessment of the fin performance, three more objective functions are studied, which are minimization of the fin surface area and maximization of the fin efficiency and effectiveness. Evaluating the heat transfer rate through the hybrid spline difference method, the well-known multi-objective genetic algorithm, namely, nondominated sorting genetic algorithm II (NSGA-II), is employed for approximating the Pareto-optimal front containing a set of tradeoff solutions in terms of different combinations of the considered five objective functions. The Pareto-optimal sensitivity is also analyzed for studying the influences of the design variables on the objective functions. As an outcome, it can be concluded that the proposed procedure would give an open choice to designers to lead to a practical stepped fin configuration.

scholarly journals On the optimal design of biased contests

2020 ◽  
Vol 15 (4) ◽  
pp. 1435-1470 ◽  
Author(s):  
Qiang Fu ◽  
Zenan Wu
Keyword(s):  
Optimal Design ◽  
General Setting ◽  
Programming Approach ◽  
Objective Functions ◽  
Equilibrium Solutions ◽  
Design Problems ◽  
Playing Field ◽  
Alternative Approach ◽  
Broad Array ◽  
Contest Design

This paper explores the optimal design of biased contests. A designer imposes an identity‐dependent treatment on contestants that varies the balance of the playing field. A generalized lottery contest typically yields no closed‐form equilibrium solutions, which nullifies the usual implicit programming approach to optimal contest design and limits analysis to restricted settings. We propose an alternative approach that allows us to circumvent this difficulty and characterize the optimum in a general setting under a wide array of objective functions without solving for the equilibrium explicitly. Our technique applies to a broad array of contest design problems, and the analysis it enables generates novel insights into incentive provisions in contests and their optimal design. For instance, we demonstrate that the conventional wisdom of leveling the playing field, which is obtained in limited settings in previous studies, does not generally hold.

scholarly journals Multi-objective optimization of three rows of film cooling holes by genetic algorithm

2020 ◽  
pp. 230-230
Author(s):  
Yaser Taheri ◽  
Meran Zargarabadi ◽  
Mehdi Jahromi
Keyword(s):  
Genetic Algorithm ◽  
Film Cooling ◽  
Control Volume ◽  
Density Ratio ◽  
Objective Functions ◽  
Optimal Point ◽  
Film Cooling Effectiveness ◽  
Design Variables ◽  
Inclination Angles ◽  
Cooling Hole

Aero-thermal optimization on multi-rows of film cooling over a flat plate has been performed to optimize the inclination angles. Hence three cylindrical holes with injection angles of ?, ?, and ? have been considered. The cooling hole has a 3 mm diameter and an inclined angle between 25 to 35 degrees. Numerical simulations were performed at a fixed density ratio of 1.25 and blowing ratio of 0.5. The control-volume method with a SIMPLEC algorithm has been used to solve the steady-state RANS equations with SST k-? turbulent model. The injection angles of the holes are selected as the design variables to perform the optimization of three rows of film cooling. In order to evaluate the performance of holes arrangement, two objective functions are defined based on aerodynamic losses and adiabatic film cooling effectiveness. The curve fitting method (CFM) is used to find the optimal point of objective functions. The optimizations have been performed using the genetic algorithm (GA) method. Results of the present study show that the best performance of three rows of cooling holes was achieved in inclined angles 25.45, 32.85 and 33.1.

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