• Advances in materials Research
• /
• v.5 no.2
• /
• pp.67-80
• /
• 2016

The objective of the present work is to optimize process parameters namely, cutting speed, feed rate, and depth of cut in milling of AISI 304 stainless steel. In this work, experiments were carried out as per the Taguchi experimental design and an $L_{27}$ orthogonal array was used to study the influence of various combinations of process parameters on surface roughness (Ra) and material removal rate (MRR). As a dynamic approach, the multiple response optimization was carried out using grey relational analysis (GRA) and desirability function analysis (DFA) for simultaneous evaluation. These two methods are considered in optimization, as both are multiple criteria evaluation and not much complicated. The optimum process parameters found to be cutting speed at 63 m/min, feed rate at 600 mm/min, and depth of cut at 0.8 mm. Analysis of variance (ANOVA) was employed to classify the significant parameters affecting the responses. The results indicate that depth of cut is the most significant parameter affecting multiple response characteristics of GFRP composites followed by feed rate and cutting speed. The experimental results for the optimal setting show that there is considerable improvement in the process.

• Nuclear Engineering and Technology
• /
• v.53 no.6
• /
• pp.1786-1795
• /
• 2021

Printed Circuit Heat Exchanger (PCHE) is a widely used heat exchanger in the supercritical carbon dioxide (sCO₂) Brayton cycle because it can work under high temperature and pressure, and has been a hot topic in Next Generation Nuclear Plant (NGNP) projects for use as recuperators and condensers. Most previous studies focused on channel structures or shapes. However, no clear advancement has so far been seen in the allover size of the PCHE. In this paper, we proposed an optimal size of the PCHE with a fixed volume. Two boundary conditions of PCHE were simulated, respectively. When the volume of PCHE was fixed, the heat transfer rate and pressure loss were picked as the optimization objectives. The Pareto front was obtained by the Multi-objective optimization procedure. We got the optimized number of PCHE channels under two different boundary conditions from the Pareto front. The comprehensive performance can be increased by 5.3% while holding in the same volume. The numerical results from this study can be used to improve the design of PCHE with straight channels.

• Journal of Korean Society of Steel Construction
• /
• v.16 no.1 s.68
• /
• pp.123-134
• /
• 2004

In this research, a multilevel decomposition technique to enhance the efficiency of the configuration optimization of truss structures was proposed. On the first level, the nonlinear programming problem was formulated considering cross-sectional areas as design variables, weight, or volume as objective function and behavior under multiloading condition as design constraint. Said nonlinear programming problem was transformed into a sequential linear programming problem. which was effective in calculation through the approximation of member forces using behavior space approach. Such approach has proven to be efficient in sensitivity analysis and different form existing shape optimization studies. The modified method of feasible direction (MMFD) was used for the optimization process. On the second level, by treating only shape design variables, the optimum problem was transformed into and unconstrained optimal design problem. A unidirectional search technique was used. As numerical examples, some truss structures were applied to illustrate the applicability. and validity of the formulated algorithm.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.283-292
• /
• 2008

The foremost criterion in the design of a Satellite Launch Vehicle(SLV) is its performance capability to boost the designated payload to the desired mission orbit; it starts from focusing on the SLV configuration to achieve the velocity requirements($}\Delta}V$) for the mission. In this paper we review an analytical approach which is suitable enough for preliminary conceptual design and is used previously to optimize stage configurations for Two Stage to Orbit SLV for Low Earth Orbit(LEO) Missions; we have extended this approach to Three Stage to Orbit SLV and compared different propellant options for the mission. The objective is to minimize the Gross Lift off Weight(GLOW). The primary performance figures of merit were the total inert weight of the SLV and the payload weight that the SLV could lift into LEO, given candidate propulsion systems. The optimization is achieved by configuring the $}\Delta}V$ between stages. A comparison of configurations of single-stage and multi-stage SLVs is made for different propellants. Based upon the optimized stage configurations a comparative performance analysis is made between Liquid and Solid fueled SLV. A 3 degree of freedom trajectory-analysis program is modeled in SIMULINK and used to conduct the performance analysis. Furthermore, a cost analysis is performed on our stage optimized SLVs. The cost estimation relationships(CER) used give us a comparison of development and fabrication costs for the Liquid vs. Solid fueled SLV in man years. The pros and cons of the production, operation ability, performance, responsiveness, logistics, price, shelf life, storage etc of both Solid and Liquid fueled SLVs are discussed. The statistics and data are used from existing or historical(real) SLV stages.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.487-496
• /
• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Structural Engineering and Mechanics
• /
• v.86 no.1
• /
• pp.119-137
• /
• 2023

The purpose of this research is to assess the performance of CBN and ceramic tools during the dry turning of gray cast iron EN GJL-350. During the turning operation, the variable machining parameters are cutting speed, feed rate, depth of cut and type of the cutting material. This contribution consists of two sections, the first one deals with the performance evaluation of four materials in terms of evolution of flank wear, surface roughness (2D and 3D) and cutting forces. The focus of the second section is on statistical analysis, followed by modeling and optimization. The experiments are conducted according to the Taguchi design L₃₂ and based on ANOVA approach to quantify the impact of input factors on the output parameters, namely, the surface roughness (Ra), the cutting force (Fz), the cutting power (Pc), specific cutting energy (Ecs). The RSM method was used to create prediction models of several technical factors (Ra, Fz, Pc, Ecs and MRR). Subsequently, the desirability function approach was used to achieve a multi-objective optimization that encompasses the output parameters simultaneously. The aim is to obtain optimal cutting regimes, following several cases of optimization often encountered in industry. The results found show that the CBN tool is the most efficient cutting material compared to the three ceramics. The optimal combination for the first case where the importance is the same for the different outputs is Vc=660 m/min, f=0.116 mm/rev, ap=0.232 mm and the material CBN. The optimization results have been verified by carrying out confirmation tests.

• Korean Chemical Engineering Research
• /
• v.55 no.3
• /
• pp.332-340
• /
• 2017

This study aims to propose a new optimization-based approach for design and analysis of the stand-alone hybrid energy supply system using renewable energy sources (RES). In the energy supply system, we include multiple energy production technologies such as Photovoltaics (PV), Wind turbine, and fossil-fuel-based AC generator along with different types of energy storage and conversion technologies such as battery and inverter. We then select six different regions of Korea to represent various characteristics of different RES potentials and demand profiles. We finally designed and analyzed the optimal RES stand-alone energy supply system in the selected regions using multiobjective optimization (MOOP) technique, which includes two objective functions: the minimum cost and the minimum $CO_2$ emission. In addition, we discussed the feasibility and expecting benefits of the systems by comparing to conventional systems of Korea. As a result, the region of the highest RES potential showed the possibility to remarkably reduce $CO_2$ emissions compared to the conventional system. Besides, the levelized cost of electricity (LCOE) of the RES-based energy system is identified to be slightly higher than conventional energy system: 0.35 and 0.46 $/kWh, respectively. However, the total life-cycle emission of $CO_2$ ($LCE_{CO2}$) can be reduced up to 470 g$CO_2$/kWh from 490 g$CO_2$/kWh of the conventional systems.

• Journal of Computational Design and Engineering
• /
• v.1 no.3
• /
• pp.187-193
• /
• 2014

One of the hottest challenges in automotive industry is related to weight reduction in sheet metal forming processes, in order to produce a high quality metal part with minimal material cost. Stamping is the most widely used sheet metal forming process; but its implementation comes with several fabrication flaws such as springback and failure. A global and simple approach to circumvent these unwanted process drawbacks consists in optimizing the initial blank shape with innovative methods. The aim of this paper is to introduce an efficient methodology to deal with complex, computationally expensive multicriteria optimization problems. Our approach is based on the combination of methods to capture the Pareto Front, approximate criteria (to save computational costs) and global optimizers. To illustrate the efficiency, we consider the stamping of an industrial workpiece as test-case. Our approach is applied to the springback and failure criteria. To optimize these two criteria, a global optimization algorithm was chosen. It is the Simulated Annealing algorithm hybridized with the Simultaneous Perturbation Stochastic Approximation in order to gain in time and in precision. The multicriteria problems amounts to the capture of the Pareto Front associated to the two criteria. Normal Boundary Intersection and Normalized Normal Constraint Method are considered for generating a set of Pareto-optimal solutions with the characteristic of uniform distribution of front points. The computational results are compared to those obtained with the well-known Non-dominated Sorting Genetic Algorithm II. The results show that our proposed approach is efficient to deal with the multicriteria shape optimization of highly non-linear mechanical systems.

• Investigative Magnetic Resonance Imaging
• /
• v.22 no.1
• /
• pp.65-70
• /
• 2018

The objective of this study was to describe a radiofrequency (RF) coil design for in vivo sodium magnetic resonance imaging (MRI) for use in small animals. Accumulating evidence has indicated the importance and potential of sodium imaging with improved magnet strength (> 7T), faster gradient, better hardware, multi-nucleus imaging methods, and optimal coil design for patient and animal studies. Thus, we developed a saddle-shaped sodium volume coil with a diameter/length of 30/30 mm. To evaluate the efficiency of this coil, bench-level measurement was performed. Unloaded Q value, loaded Q value, and ratio of these two values were estimated to be 352.8, 211.18, and 1.67, respectively. Thereafter, in vivo acquisition of sodium images was performed using normal mice (12 weeks old; n = 5) with a two-dimensional gradient echo sequence and minimized echo time to increase spatial resolution of images. Sodium signal-to-noise ratio in mouse kidneys (renal cortex, medulla, and pelvis) was measured. We successfully acquired sodium MR images of the mouse kidney with high spatial resolution (approximately 0.625 mm) through a combination of sodium-proton coils.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.10
• /
• pp.1021-1028
• /
• 2011

The objective of this paper is to optimize the design parameters of a novel mechanism for a robotic knee orthosis. The feature of the proposed knee othosis is to drive a knee joint with independent actuation during swing and stance phases, which can allow an actuator with fast rotation to control swing motions and an actuator with high torque to control stance motions, respectively. The quadriceps device operates in five-bar links with 2-DOF motions during swing phase and is changed to six-bar links during stance phase by the contact motion to the patella device. The hamstring device operates in a slider-crank mechanism for entire gait cycle. The suggested kinematic model will allow a robotic knee orthosis to use compact and light actuators with full support during walking. However, the proposed orthosis must use additional linkages than a simple four-bar mechanism. To maximize the benefit of reducing the actuators power by using the developed kinematic design, it is necessary to minimize total weight of the device, while keeping necessary actuator performances of torques and angular velocities for support. In this paper, we use a SGA (Simple Genetic Algorithm) to minimize sum of total link lengths and motor power by reducing the weight of the novel knee orthosis. To find feasible parameters, kinematic constraints of the hamstring and quadriceps mechanisms have been applied to the algorithm. The proposed optimization scheme could reduce sum of total link lengths to half of the initial value. The proposed optimization scheme can be applied to reduce total weight of general multi-linkages while keeping necessary actuator specifications.