Won-Sik Joo

Trade-off analysis in multi-objective optimization using Chebyshev orthogonal polynomials

In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps : the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.

A Study on Fatigue Damage Modeling Using Neural Networks

Fatigue crack growth and life have been estimated based on established empirical equations. In this paper, an alternative method using artificial neural network (ANN) -based model developed to predict fatigue damages simultaneously. To learn and generalize the ANN, fatigue crack growth rate and life data were built up using in-plane bending fatigue test results. Single fracture mechanical parameter or nondestructive parameter can’t predict fatigue damage accurately but multiple fracture mechanical parameters or nondestructive parameters can. Existing fatigue damage modeling used this merit but limited real-time damage monitoring. Therefore, this study shows fatigue damage model using backpropagation neural networks on the basis of X-ray half breadth ratioB/B0, fractal dimensionDf and fracture mechanical parameters can estimate fatigue crack growth rateda/dN and cycle ratioN/Nf at the same time withinengineering limit error (5%).

Response Surface Approximation for Fatigue Life Prediction and Its Application to Multi-Criteria Optimization With a Priori Preference Information

In this paper, a versatile multi-criteria optimization concept for fatigue life prediction is introduced. Multi-criteria decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

Size Effect on the Modulus of Rupture in Automotive Ceramic Monolithic Substrate using Optimization and Response Surface Method

Since the monolithic ceramic substrate was in introduced for automotive catalytic converters, the durability of the substrate has been a continuing requirement to reduce the emission, gas of vehicle. The substrate can occupy a volume as small as 82 and as large as 8200 to provide the required substrate for catalytic activity. The long-term durability varies with the size of the substrate from manufacture`s point of view. Therefore this study presents that the response surface model using central composite design can explain size effect on the modulus of rupture in a cordierite ceramic monolithic substrate.

A Sequential Optimization Algorithm Using Metamodel-Based Multilevel Analysis

An efficient sequential optimization approach for metamodel was presented by Choi et al. (13) This paper describes a new approach of the multilevel optimization method studied in Refs. (2) and (20,21). The basic idea is concerned with multilevel iterative methods which combine a descent scheme with a hierarchy of auxiliary problems in lower dimensional subspaces. After fitting a metamodel based on an initial space filling design, this model is sequentially refined by the expected improvement criterion. The advantages of the method are that it does not require optimum sensitivities, nonlinear equality constraints are not needed, and the method is relatively easy to understand and use. As a check on effectiveness, the proposed method is applied to an engineering example.

Experimental Estimation of Thermal Durability in Ceramic Catalyst Supports for Passenger Car

Ceramic honeycomb structures have performed successfully as catalyst supports for meeting hydrocarbon, carbon monoxide and nitrous emissions standards for gasoline-powered vehicles. Three-way catalyst converter has to withstand high temperature and thermal stress due to pressure fluctuations and vibrations. Thermal stress constitutes a major portion of the total stress which the ceramic catalyst support experiences in service. In this study, temperature distribution was measured at ceramic catalyst supports. Thermal durability was evaluated by power series dynamic fatigue damage model. Radial temperature gradient was higher than axial temperature gradient. Thermal stresses depended on direction of elastic modulus. Axial stresses are higher than tangential stresses. Tangential and axial stresses remained below thermal fatigue threshold in all engine operation ranges.

Structural Optimization Using Tabu Search in Discrete Design Space

Structural optimization has been carried out in continuous or discrete design space. Methods for continuous design have been well developed though they are finding the local optima. On the contrary, the existing methods for discrete design are extremely expensive in computational cost or not robust. In this research, an algorithm using tabu search is developed fur the discrete structural designs. The tabu list and the neighbor function of the Tabu concepts are introduced to the algorithm. It defines the number of steps, the maximum number for random searches and the stop criteria. A tabu search is known as the heuristic approach while genetic algorithm and simulated annealing algorithm are attributed to the stochastic approach. It is shown that an algorithm using the tabu search with random moves has an advantage of discrete design. Furthermore, the suggested method finds the reliable optimum for the discrete design problems. The existing tabu search methods are reviewed. Subsequently, the suggested method is explained. The mathematical problems and structural design problems are investigated to show the validity of the proposed method. The results of the structural designs are compared with those from a genetic algorithm and an orthogonal array design.

Multi-objective Optimization in Discrete Design Space using the Design of Experiment and the Mathematical Programming

A recent research and development has the requirement for the optimization to shorten design time of modified or new product model and to obtain more precise engineering solution. General optimization problem must consider many conflicted objective functions simultaneously. Multi-objective optimization treats the multiple objective functions and constraints with design change. But, real engineering problem doesn`t describe accurate constraint and objective function owing to the limit of representation. Therefore this study applies variance analysis on the basis of structure analysis and DOE to the vertical roller mill fur portland cement and proposed statistical design model to evaluate the effect of structural modification with design change by performing practical multi-objective optimization considering mass, stress and deflection.

A Study on Bending Behavior of Tunnel Support

The tunnel stability concerned with safety is very important in coal production process. The tunnel supports made by the GI beam has been used in domestic coal mine tunnels, and the GI beam was connected with another by the fish plate. It is necessary to analysis for the bending problems of the fish plate due to the rock pressure in some domestic mine tunnels. Therefore, this study proposes the application possibility of the optimization algorithms for the problem searching a load condition that bring about bending problem in tunnels. Consequently, in order to investigate the load conditions, desirability function as one of the optimization methods to study the bending behavior of tunnel supports was applied.