• Advances in materials Research
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• 제1권2호
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• pp.129-146
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• 2012

Polycrystalline diamond is an ideal material for parts with micro-holes and has been widely used as dies and cutting tools in automotive, aerospace and woodworking industries due to its superior wear and corrosion resistance. In this research paper, the modeling and simultaneous optimization of multiple performance characteristics such as material removal rate and surface roughness of polycrystalline diamond (PCD) with ultrasonic machining process has been presented. The fuzzy logic and taguchi's quality loss function has been used. In recent years, fuzzy logic has been used in manufacturing engineering for modeling and monitoring. Also the effect of controllable machining parameters like type of abrasive slurry, their size and concentration, nature of tool material and the power rating of the machine has been determined by applying the single objective and multi-objective optimization techniques. The analysis of results has been done using the MATLAB 7.5 software and results obtained are validated by conducting the confirmation experiments. The results show the considerable improvement in S/N ratio as compared to initial cutting conditions. The surface roughness of machined surface has been measured by using the Perthometer (M4Pi, Mahr Germany).

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2001년도 추계학술발표대회 논문집
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• pp.70-73
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• 2001

The layup optimization by genetic algorithm (GA) for the interlaminar strength of laminated composites with free edge is presented. For the calculation of interlaminar stresses of composite laminates with free edges, extended Kantorovich method is applied. In the formulation of GA, repair strategy is adopted for the satisfaction of given constraints. In order to consider the bounded uncertainty of material properties, convex modeling is used. Results of GA optimization with scattered properties are compared with those of optimization with nominal properties. The GA combined with convex modeling can work as a practical tool for maximum interlaminar strength design of laminated composite structures, since uncertainties are always encountered in composite materials and the optimal results can be changed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Computers and Concrete
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• 제14권4호
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• pp.445-462
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• 2014

To solve structural optimization problems, it is necessary to integrate a structural analysis package and an optimization package. There have been many packages that can be employed to analyze reinforced concrete plane frames. However, because most structural analysis packages suffer from closeness of systems, it is very difficult to integrate them with optimization packages. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrates Design, Analysis, Modeling, Definition, and Optimization phases into an integration environment as follows. (1) Design: first generate many possible structural design alternatives. Each design alternative consists of many design variables X. (2) Analysis: employ the structural analysis software to analyze all structural design alternatives to obtain their internal forces and displacements. They are the response variables Y. (3) Modeling: employ artificial neural networks to build the models Y=f(X) to obtain the relationship functions between the design variables X and the response variables Y. (4) Definition: employ the design variables X and the response variables Y to define the objective function and constraint functions. (5) Optimization: employ the optimization software to solve the optimization problem consisting of the objective function and the constraint functions to produce the optimum design variables. The RC frame optimization problem was examined to evaluate the DAMDO approach, and the empirical results showed that it can be solved by the approach.

• Advances in Computational Design
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• 제1권3호
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• pp.235-251
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• 2016

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

• Smart Structures and Systems
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• 제26권4호
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• pp.533-544
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• 2020

Effective bridge maintenance reduces bridge operation costs and extends its service life. The possibility of storing bridge life-cycle data in a 3D parametric model of the bridge through Bridge Information Modeling (BrIM) provides new opportunities to enhance current practices of bridge maintenance management. This study develops a Decision Support System (DSS), namely BrDSS, which employs BrIM and an efficient optimization model for bridge maintenance planning. The BrIM model in BrDSS extracts basic data of elements required for the optimization process and visualizes the inspection data and the optimization results to the user to help in decision makings. In the optimization module of the DSS, the specifically formulated Genetic Algorithm (GA) eliminates the chances of producing infeasible solutions for faster convergence. The practicality of the presented DSS was explored by utilizing the DSS in the maintenance planning of a bridge under operation in the southwest of Iran.

• Earthquakes and Structures
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• 제7권3호
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• pp.271-294
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• 2014

In recent years, along with the advances made in performance-based design optimization, the need for fast calculation of response parameters in dynamic analysis procedures has become an important issue. The main problem in this field is the extremely high computational demand of time-history analyses which may convert the solution algorithm to illogical ones. Two simplifying strategies have shown to be very effective in tackling this problem; first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication, second, wavelet analysis of earthquake records decreasing the number of acceleration points involved in time-history loading. In this paper, we try to develop an efficient framework, using both strategies, to solve the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency (FEMA) recommended design specifications. The results from numerical application of the proposed framework demonstrate the capabilities of the framework in solving the present multi-objective optimization problem.

• 한국자동차공학회논문집
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• 제3권1호
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• pp.33-44
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• 1995

As the occupant safety receives more attention from automobile industries. protection systems have been developed quite well. Developed protection systems must be evaluated through real tests in crash environment Since the real tests are extremely expensive. computer simulations are replaced for some prediction of the real test In the computer simulation. it is very crucial to express the real environment precisely in the modeling precess. The energy absorbing(EA) steering system has a very important rote in vehicle crashes because the occupant can hit the system directly. In this study. the EA steering system is modeled precisely. analyzed for the safely and designed by an optimization technology. First. the EA steering system is disassembled by parts and modeled by segments and joints. The segments are modeled by rigid bodies in motion and they have resistances in contact. Spring-damper elements and force-deflection curves are utilized to represent the joints. The body block test is cal lied out to validate. the modeling. When the test results are not enough for the detailed modeling. the differences between tests and simulations are minimized to calculate unknown parameters using optimization. The established model is applied to a crash simulation of a full-car model and tuned again. After the modeling is finished. components of the steering system are designed by an optimization algorithm. In the optimization process. the compound injury of a driver is defined and minimized to determine the chracteristics of the components. The second. order approximation algorithm has been adopted for the optimization.

• 한국항공우주학회지
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• 제37권1호
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• pp.42-54
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• 2009

비행체의 최적궤적문제에서는 최적화의 수렴성 및 효율성을 위해 3자유도 운동역학모델이 이용되며, 비선형 추종제어를 위해서는 6자유도 비선형 운동모델이 이용된다. 따라서, 3자유도 운동역학모델을 통해 획득한 최적궤적을 비선형 추종제어의 기준궤적으로 사용하는 경우에 두 모델간의 상이성으로 인한 문제가 발생하게 되며 성능이 보장하지 못한다. 본 논문에서는 이러한 두 모델간의 차이를 완화시키기 위한 새로운 최적궤적 생성 방법을 제안하였으며, 성층권비행선의 실제 구속조건과 성능조건 및 제트 스트림을 고려하여 최적궤적을 생성하고 기존 결과와 비교함으로써 제안한 방법의 장점을 검증하였다.

• 융복합기술연구소 논문집
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• 제4권2호
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• pp.1-9
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• 2014

This paper examines feature-based reconstruction algorithm using feature-based modeling and based on topology optimization technology, which aims to achieve a minimal volume weight and to satisfy user-defined constraints such as stress, deformation related conditions. The finite element model after topology optimization allows us to remove some region of a solid model for predefined volume requirement. The stress or deformation distribution resulted from finite element analysis enables us to add some material to the solid model for a robust structure. For this purpose, we propose a feature-based redesign algorithm which inserts negative features to the solid model for material removal and positive features for material addition, and we introduce a bisection method which searches an optimal structure by iteratively applying the feature-based redesign algorithm. Several examples are considered to illustrate the proposed algorithms and to demonstrate the effectiveness of the present approach.