• Korean Journal of Computational Design and Engineering
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• v.21 no.4
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• pp.389-396
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• 2016

The necessity for environment-friendly material development has emerged in the recent automotive field due to stricter regulations on fuel economy and environmental concerns. Accordingly, the automotive industry is paying attention to carbon fiber reinforced plastic (CFRP) material with high strength and stiffness properties while the lightweight. In this study, we determine a shape of lower control arm (LCA) for maximizing the strength and stiffness by optimizing the thickness of each layer when the stacking angle is fixed due to the CFRP manufacturing problems. Composite materials are laminated in the order of $0^{\circ}$, $90^{\circ}$, $45^{\circ}$, and $-45^{\circ}$ with a symmetrical structure. For the approximate optimal design, we apply a sequential two-point diagonal quadratic approximate optimization (STDQAO) and use a process integrated design optimization (PIDO) code for this purpose. Based on the physical properties calculated within a predetermined range of laminate thickness, we perform the FEM analysis and verify whether it satisfies the load and stiffness conditions or not. These processes are repeated for successive improved objective function. Optimized CFRP LCA has the equivalent stiffness and strength with light weight structure when compared to conventional aluminum design.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.5
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• pp.8-14
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• 2013

In this study, we propose the method of optimally selecting material of front pillar (A-pillar) and package tray for minimizing weight while satisfying vehicle requirements on static stiffness and dynamic stiffness. First, we formulate a material selection optimization problem. Next, we establish the CAE procedure of evaluating static stiffness and dynamic stiffness. Then, to enhance the efficiency of design work, we integrate and automate the established CAE procedure using a commercial process integration and design optimization (PIDO) tool, PIAnO. For effective optimization, we adopt the approach of metamodel based approximate optimization. As a sampling method, an orthogonal array (OA) is used for selecting sampling points. The response values are evaluated at the sampling points and then these response values are used to generate a metamodel of each response using the radial basis function regression (RBFr). Using the RBFr models, optimization is carried out an evolutionary algorithm that can handle discrete design variables. Material optimization result reveals that the weight is reduced by 49.8% while satisfying all the design constraints.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.189-195
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• 2000

The mathematical solutions of the stability convergence are important problems in system control. In this paper such problems are analyzed and resolved for system control using multilayer neural networks. We describe an algorithm to control an unknown nonlinear system with a disturbance, using a multilayer neural network. We include a disturbance among the modeling error, and the weight update rules of multilayer neural network are derived to satisfy Lyapunov stability. The overall control system is based upon the feedback linearization method. The weights of the neural network used to approximate a nonlinear function are updated by rules derived in this paper . The proposed control algorithm is verified through computer simulation. That is as the weights of neural network are updated at every sampling time, we show that the output error become finite within a relatively short time.

• Geomechanics and Engineering
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• v.15 no.4
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• pp.937-945
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• 2018

A probabilistic study of a reinforced earth wall in a frictional soil using the surface response methodology (RSM) is presented. A deterministic model based on numerical simulations is used (Abdelouhab et al. 2011, 2012b) and the serviceability limit state (SLS) is considered in the analysis. The model computes the maximum horizontal displacement of the wall. The response surface methodology is utilized for the assessment of the Hasofer-Lind reliability index and is optimized by the use of a genetic algorithm. The soil friction angle and the unit weight are considered as random variables while studying the SLS. The assumption of non-normal distribution for the random variables has an important effect on the reliability index for the practical range of values of the wall horizontal displacement.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.58-61
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• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

• Nuclear Engineering and Technology
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• v.23 no.1
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• pp.12-19
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• 1991

An engineering approach for estimating the stress intensity factors of a semi-elliptical crack is presented. An approximate 2-dimensional approach solution for semi-elliptical crack is derived in terms of simple equation, through weight function technique, by reflecting on the physical character of cracks.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.6
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• pp.954-960
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• 2012

Nowadays, rapidly changing and unstable global economic environments request a lot of roles to engineers. In this situation, product should be designed to make more profit by cost down and to satisfy distinguished performance comparing to other competitive ones. In this research, the optimization design of the industrial robot casting will be done. The weight and deflection have to be reduced as objective functions and stress has to be constrained under some constant value. To reduce time cost, CCD (Central Composite Design) will be used to make experimental design. And RSM (Response Surface Methodology) will be taken to make regression model for objective functions and constraint function. Finally, optimization will be done with Genetic Algorithm. In this problem, the objective functions are multiple, so NSGA-II which is brilliant and efficient for such a problem will be used. For the solution quality check, the diversity between Pareto solutions will be also checked.

• Steel and Composite Structures
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• v.8 no.5
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• pp.343-359
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• 2008

In the present study, an efficient method for the optimum design of three-dimensional (3D) steel framed structures is proposed. In this method, in addition to choosing the best position of columns based on architectural requirements, the optimum cross-sectional dimensions of elements are determined. The preliminary design variables are considered as the number of columns in structural plan, which are determined by a direct optimization method suitable for discrete variables, without requiring the evaluation of derivatives. After forming the geometry of structure, the main variables of the cross-sectional dimensions are evaluated, which satisfy the design constraints and also achieve the least-weight of the structure. To reduce the number of finite element analyses and the overall computational time, a new third order approximate function is introduced which employs only the diagonal elements of the higher order derivatives matrices. This function produces a high quality approximation and also, a robust optimization process. The main feature of the proposed techniques that the higher order derivatives are established by the first order exact derivatives. Several examples are solved and efficiency of the new approximation method and also, the proposed method for the best position of columns in 3D steel framed structures is discussed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.85-95
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• 2021

Structure design sensitivity was evaluated using the orthogonal array experimental method for passive-type deck support frame (DSF) developed for float-over installation of the offshore plant. Moreover, approximation characteristics were also reviewed based on various meta-models. The minimum weight design of the DSF is significantly important for securing both maneuvering performance and buoyancy of a ship equipped with the DSF and guaranteeing structural design safety. The performance strength of the passive type DSF was evaluated through structure analysis based on the finite element method. The thickness of main structure members was applied to design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experimental method and analysis of variance. The optimum design case was also identified from the orthogonal array experiment results. Various meta-models, such as Chebyshev orthogonal polynomial, Kriging, response surface method, and radial basis function-based neural network, were generated from the orthogonal array experiment results. The results of the orthogonal array experiment were validated using the meta-modeling results. It was found that the radial basis function-based neural network among the meta-models could approximate the design space of the passive type DSF with the highest accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.