• Advances in materials Research
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• v.13 no.1
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.225-226
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• 2012

Nowadays the widely used media in architecture include visualizations, animations and three-dimensional models. An optimized 3D digital method using active CAM(Computer Aided Manufacturing) and CNC(Computerized Numerical Control) imaging is developed for accurate shape and 3D measurements in freeform buildings in this paper. In contrast to a conventional building using auto CAD system and others, the proposed active digital optimization is based on a combination of 3D numerical data and parametric 3D model. The objective of this paper is therefore to present digital optimization method for constructability of freeform building. The 3D digital optimization method is appropriate to serious variations in freeform shape. The developed digital optimization method is necessary to be carried out to verify the robustness and accuracy for constructability.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.124-129
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• 2013

Electro-Mechanical Batteries have important advantages compared with chemical batteries, especially in Low Earth Orbit satellites applications. High speed, slotless, external rotor, brushless DC machines are proposed and used in these systems as Motor/Generator. A simplified analytic design method is given for this type of machines and, the optimization of machine in order to have maximum efficiency and minimum volume and weight are given in this paper. Particle swarm optimization (PSO) is used as the optimization algorithm and the finite element-based simulations are used to confirm the design and optimization process and show less than 6% error in parametric design.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.4
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• pp.332-337
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• 2018

This paper presents the design optimization of fuel sensor location used to measure remained fuel amount in aircraft conformal fuel tank. The conformal fuel tank is utilized to expand the mission range in airplane, and the sensor location is a critical design variable determining the measurement accuracy. In this work, the sensor location is optimized to minimize unmeasurable fuel amount due to non-contact between fuel and sensor. The simplified model is prepared from the conformal fuel tank CATIA model, and the unmeasurable fuel amount is calculated. Then, the optimization is performed using MATLAB optimization solver. The optimized sensor location is validated by comparing with the location obtained using parametric study.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.716-726
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• 2010

In this research, design optimization of an aircraft wing has been performed using the fully automated Multidisciplinary Design Optimization (MDO) framework, which integrates aerodynamic and structural analysis considering nonlinear structural behavior. A computational fluid dynamics (CFD) mesh is generated automatically from parametric modeling using CATIA and Gambit, followed by an automatic flow analysis using FLUENT. A computational structure mechanics (CSM) mesh is generated automatically by the parametric method of the CATIA and visual basic script of NASTRAN-FX. The structure is analyzed by ABAQUS. Interaction between CFD and CSM is performed by a fully automated system. The Response Surface Method (RSM) is applied for optimization, helping to achieve the global optimum. The optimization design result demonstrates successful application of the fully automated MDO framework.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.6
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• Journal of Mechanical Science and Technology
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• v.20 no.2
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• pp.251-261
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• 2006

The objective of this work is to apply artificial neural networks for solving inverse problems in the structural optimization of a fiber optic pressure sensor. For the sensor under investigation to achieve a desired accuracy, the change in the distance between the tips of the two fibers due to the applied pressure should not interfere with the phase change due to the change in the density of the air between the two fibers. Therefore, accurate dynamic analysis and structural optimization of the sensor is essential to ensure the accuracy of the measurements provided by the sensor. To this end, a normal mode analysis and a transient response analysis of the sensor were performed by combining commercial finite element analysis package, MSC/NASTRAN, and MATLAB. Furthermore, a parametric study on the design of the sensor was performed to minimize the size of the sensor while fulfilling a number of constraints. In performing the parametric study, the need for a relationship between the design parameters and the response of the sensor was fulfilled by using a neural network. The whole process of the dynamic analysis using commercial finite element analysis package and the parameter optimization of the sensor were automated within the MATLAB environment.

• Transactions of the Society of Information Storage Systems
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• v.4 no.1
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• pp.6-12
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• 2008

The paper deals with the numerical analysis and design optimization of polysilicon micro thermal flexure actuator. The deflection of a thermal actuator is implicitly related to the actuation time so that such deflection is to be maximized under the consideration of structural performances such as maximum stress and natural frequencies. At first, the structural formulation of a thermal actuator is reviewed, and its CAE based simulation is performed to verify the numerical model. A parametric study is then conducted to identify the mainly effective design variables. Finally, the design of a micro thermal actuator is explored in the context of deterministic optimization and reliability based design optimization in the present study.

• International Journal of Fluid Machinery and Systems
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• v.9 no.3
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• pp.213-221
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• 2016

Due to the nature of cavitation numerical analyses, computational optimization of a pump with respect to the cavitation properties is extremely demanding. In this paper it is shown how a combination of Transient Blade Row (TBR) method and some simplifications can be used for making the optimization process more efficient and thus possible on current generation of hardware. The aim of the paper is not the theory of hydraulic design. Instead, the practical aspects of numerical optimization are shown. This is done on an example of a radial pump and a combination of ANSYS CFX, ANSYS software tools and custom scripts is used. First, a comparison of TBR and fully-transient simulation is made. Based on the results, the TBR method is chosen and a parametric model assembled. Design of Experiment (DOE) table is computed and the results are used for sensitivity analysis. As the last step, the final design is created and computed as fully-transient. In conclusion, the results are discussed.

• The KSFM Journal of Fluid Machinery
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• v.17 no.4
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• pp.19-29
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• 2014

In addition to the development of micro hydro turbine, the challenge in micro hydro turbine design as sustainable hydro devices is focused on the optimization of turbine runner blade which have decisive effect on the turbine performance to reach higher efficiency. A multi-objective optimization method to optimize the performance of runner blade of propeller turbine for micro turbine has been studied. For the initial design of planar blade cascade, singularity distribution method and the combination of the Bezier curve parametric technology is used. A non-dominated sorting genetic algorithm II(NSGA II) is developed based on the multi-objective optimization design method. The comparision with model test show that the blade charachteristics is optimized by NSGA-II has a good efficiency and load distribution. From model test and scale up calculation, the maximum prototype efficiency of the runner blade reaches as high as 90.87%.