• 한국공간정보시스템학회:학술대회논문집
• /
• 2004.05a
• /
• pp.43-52
• /
• 2004

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algerian was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• Journal of Korean Association for Spatial Structures
• /
• v.2 no.3 s.5
• /
• pp.93-102
• /
• 2002

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithms. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithms. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithms were verified by applying the algorithm to optimum design examples

• Advances in nano research
• /
• v.14 no.2
• /
• pp.155-164
• /
• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• Proceeding of KASS Symposium
• /
• 2005.05a
• /
• pp.236-245
• /
• 2005

This paper was developed the discrete optimum design program by the refined fuzzy-genetic algorithms based on the genetic algorithms and fuzzy theory. The optimum design of this paper can perform both size and shape optimum design for planar and spacial steel structures. In this paper, the objective function is the weight of steel structures and the constraints are the design limits defined by design and buckling strengths, displacements and thicknesses. The design variables are dimensions and coordinates of steel sections. Design examples are given to show the applicability of the discrete optimum design program of this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.1163-1164
• /
• 2013

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2012.05a
• /
• pp.403-407
• /
• 2012

The purpose of this study is to find the optimum skirt size for a composite pressure vessel using optimum analysis technique. The size optimization for skirt shape of a composite pressure vessel was conducted using sub-problem approximation method and batch processing codes programmed by APDL(ANSYS Parametric Design Language). The thickness and length of skirt part were selected as design variables for the optimum analysis. The objective function and constraints were chosen as weight and displacement of skirt part, respectively. The numerical results showed that the weight of skirt of a composite pressure vessel would be saved by maximum 4.38% through the size optimization analysis for the skirt shape.

• Journal of Korean Association for Spatial Structures
• /
• v.1 no.1 s.1
• /
• pp.125-134
• /
• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.8 no.6
• /
• pp.602-614
• /
• 2016

It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

• International Journal of Automotive Technology
• /
• v.6 no.6
• /
• pp.643-655
• /
• 2005

The frontal crash optimization of S-shaped closed-hat section member using the homogenization method, design of experiment (DOE) and response surface method (RSM) was studied. The optimization to effectively absorb more crash energy was studied to introduce the reinforcement design. The main focus of design was to decide the optimum size and thickness of reinforcement. In this study, the location of reinforcement was decided by homogenization method. Also, the effective size and thickness of reinforcements was studied by design of experiments and response surface method. The effects of various impact velocity for reinforcement design were researched. The high impact velocity reinforcement design showed to absorb the more crash energy than low velocities design. The effect of size and thickness of reinforcement was studied and the sensitivity of size and thickness was different according to base thickness of model. The optimum size and thickness of the reinforcement has shown a direct proportion to the thickness of base model. Also, the thicker the base model was, the effect of optimization using reinforcement was the bigger. The trend curve for effective size and thickness of reinforcement using response surface method was obtained. The predicted size and thickness of reinforcement by RSM were compared with results of DOE. The results of a specific dynamic mean crushing loads for the predicted design by RSM were shown the small difference with the predicted results by RSM and DOE. These trend curves can be used as a basic guideline to find the optimum reinforcement design for S-shaped member.

• Journal of Korean Society of Steel Construction
• /
• v.13 no.6
• /
• pp.673-681
• /
• 2001

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithm. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the design points selected form the genetic process. The evolutionary process evaluates the survivability of the design points. The evolutionary process evaluates the survivability of the design points selected form the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.