Narrow Results

We studied the flow-induced vibration (FIV) and fretting wear of fuel rod with grid relaxation. According to the flow distribution around a type of pressurized water reactor (PWR) fuel rod, the power spectral density (PSD) is obtained to characterize the turbulence excitation. By combining the correlation of PSD test parameters, the mean square value of the vibration displacement of each rod mode is found, and then the wear depth of dimple position is calculated based on the ARCHARD wear formula. The grids may relax due to inaccurate manufacturing, fuel transportation and in-core irradiation. The absence of grid clamping force would significantly influence the rod mode and thereby changes its FIV responses. Simulation results show that the failure of the leaf spring has negligible effect on the rod natural frequency whereas the dimple failure near the location with larger FIV amplitude has a much significant effect. The lateral flow velocities at the inlet and outlet of the core are larger. For the fully clamped fuel rod, the responses amplitude of turbulent excitation at the bottom and top of the fuel rod are larger. This is even more obvious with a failed dimple at these locations. Comparatively, the effect of dimple support failure in the middle is less influential. The influence of dimple support failure on the rod wear depth depicts basically the same trend as on the maximum FIV amplitude.

The Taguchi robust design concept is combined with the multi-objective deterministic optimization method to overcome single point design problems in Aerodynamics. Starting from a statistical definition of stability, the method finds, Nash equilibrium solutions for performance and its stability simultaneously.

In this paper, the Selected Objects Extraction (SOE) CNN is generalized to Directional Extraction (DE) CNN which enhances the capabilities of the SOE CNN. Using analytical approaches, a theorem to design robust templates for DE CNN is established. The theorem provides parameter inequalities to determine parameter intervals in which the templates can implement the corresponding functions. Based on the theorem, an optimal model is set up to design optimally robust templates for DE CNN. Two examples are provided to illustrate the effectiveness of the theorem.

This paper proposes fuzzy-based multi-objective design optimization approach for the optimal analysis of buried pipe based on the expected value of a fuzzy output variable when the membership function is computed. The design of pipe structures is usually associated with uncertainties. Therefore, the principle of fuzzy set and a multi-objective optimization algorithm is applied to account for the variabilities associated with the uncertain parameters to ensure an acceptable performance against the impact of uncertainties. Different methods such as deterministic and non-deterministic methods have been proposed to model the effect of uncertainties and analyse the performance of engineering structure in the literature. Herein, a fuzzy-based uncertainty modelling approach that employs the optimal performance of a hybrid GA-GAM for the analysis of a buried pipeline is proposed. The purpose of the strategy is to optimise the design variable while considering the adverse effect of the uncertain fuzzy variables and variability of the structural performance. The uncertain fuzzy variable is used in the analysis to take into account the subjective nature of the corrosion process, while the entropy of a fuzzy variable used as a global measure of variable dispersion is employed to measure the variability and sensitivity of the structural response. The outcome of the fuzzy-based multi-objective design optimization provides a set of optimal solution for the analysis of fuzzy structure. Finally, the applicability and characteristic of this method are demonstrated using a numerical example, and the outcome denotes acceptable analytical tools for design engineers and can be applied to analyse other engineering structures.

The major purposes of this research are to improve the process yield rate for air cleaners in Toyota Corona vehicles and to find the robust parameters of this process by using the Taguchi method and design for the experiments. Furthermore, in order to reduce the scrap cost, improve the quality of plastic injection and decrease environmental pollution, we aim to reduce the occurrence of nonconformities and increase the use of recycled material.

Within the analysis of the injection mold, we would like to study the correlation between injecting conditions and nonconformities by using the orthogonal table of L₁₈. Based on the results of the S/N ratio, ANOVA, and response surface methods, we have given suggestions for the optimal conditions for the specifications of the parameters and the standard of the process. Moreover, the results of this research can be applied to other products.

Finally, we expect to develop software which can apply the Taguchi methodology and is user friendly for both suppliers and users of video instruction. Furthermore, the Taguchi method will be generally applied to other component processing methods involving other vehicles.

Although process design optimization issues have received considerable attention from researchers for more than several decades, and a number of methodologies for modeling and optimizing the process have been developed, there is still ample room for improvement. Most research work has rarely considered the use of raw data from a manufacturing process database into the process design. However, the use of cumulative raw data can be a vital component in optimizing processes. To address this, we propose a new process design procedure called robust-Bayesian data mining (RBDM). First, we show how data mining techniques and a correlation-based feature selection (CBFS) method can be applied effectively to the selection of significant factors. Second, we then show how RBDM can be incorporated into robust design. Third, we present how the proposed RBDM estimates process parameters by considering the concept of robustness of the estimated parameters while incorporating the concept of noise factors. Finally, we present numerical examples to illustrate the efficiency of the proposed RBDM as a design tool for optimizing manufacturing processes.

The increasing customer awareness and global competition have forced manufacturers to capture the entire life cycle issues during product design and development stage. The thorough understanding of product behavior (degradation process) and various uncertainties associated with product performance is paramount to produce reliable and robust design. This paper proposes a multi-objective framework for reliability-based robust design optimization, which captures degradation behavior of quality characteristics to provide optimal design parameters. The objective function of the multi-objective optimization problem is defined as quality loss function considering both desirable and undesirable deviations between target values and the actual results. The degradation behavior is captured by using empirical model to estimate amount of degradation accumulated in time t. The applicability of the proposed methodology is demonstrated by considering a leaf spring design problem.

Tolerance design technique balances the expected quality loss due to variations of the system performance and the cost due to controlling these variations. Measures of quality are discussed and quality loss function is used for tolerance design. The goal is to minimize the total loss that consists of the quality loss to the customer and the cost increase to the producer. The design methodologies are presented for the tolerances of products before shipping to the customer and the tolerances of lower-level characteristics. The approaches to tolerance design for components and subsystems are also demonstrated using the variation transfer function. Examples are given as illustrations of the methodology.

The common version of Genichi Taguchi's parameter design entails a marginal analysis procedure for determining parameter settings that will maximize or minimize a response. Unless all significant parameter interactions are known a priori and provided for in the orthogonal array of the parameter design experiment, conclusions drawn from marginal analysis will not necessarily be correct. In this paper, the probability that the routine parameter design procedure will actually succeed in realizing the optimization objective is discussed, followed by illustrations based on data from the commonly cited literature.

The discrepancy between the analytically determined buckling load of perfect cylindrical shells and experimental test results is traced back to imperfections. The most frequently used guideline for design of cylindrical shells, NASA SP-8007, proposes a deterministic calculation of a knockdown factor with respect to the ratio of radius and wall thickness, which turned out to be very conservative in numerous cases and which is not intended for composite shells. In order to determine a lower bound for the buckling load of an arbitrary type of shell, probabilistic design methods have been developed. Measured imperfection patterns are described using double Fourier series, whereas the Fourier coefficients characterize the scattering of geometry. In this paper, probabilistic analyses of buckling loads are performed regarding Fourier coefficients as random variables. A nonlinear finite element model is used to determine buckling loads, and Monte Carlo simulations are executed. The probabilistic approach is used for a set of six similarly manufactured composite shells. The results indicate that not only geometric but also nontraditional imperfections like loading imperfections have to be considered for obtaining a reliable lower limit of the buckling load. Finally, further Monte Carlo simulations are executed including traditional as well as loading imperfections, and lower bounds of buckling loads are obtained, which are less conservative than NASA SP-8007.

The European Commission 6th Framework Project COCOMAT was a four-and-a-half-year project (2004 to mid-2008) aimed at exploiting the large reserve of strength in composite structures through more accurate prediction of collapse. In the experimental work packages, significant statistical variations in buckling behaviour and ultimate loading were encountered. During the experiments for the COCOMAT project, it was recognised that there was a gap in knowledge about the effect of initial defects and variations in the input variables of both the experimental and simulated panels. The effect of the defects and variations in the experimental panel resulted in some failure modes that were not predicted with the finite element modelling. This led to the development of stochastic algorithms to relate variations in boundary conditions, material properties and geometries to the variation in buckling modes and compression loads up to the first failure. This paper shows the development of a stochastic methodology to identify the impact of variation in input parameters on the response of stiffened composite panels and the development of a robust index to support the evaluation of panel designs. The stochastic analysis included the generation of metamodels that allow quantification of the impact that the inputs have on the response using two first order variables, influence and sensitivity. These variables were then used to derive the robust indices to quantify the response of two COCOMAT panels that were experimentally tested, including the response of the panels to simulated damage. The robust indices that are shown in this paper are functions of the robustness parameter which has been recommended in the final Design Guidelines for the COCOMAT project to measure the effects of scatter found in postbuckling loads.

Developed in Japan and becoming increasingly talked about under the name 'Taguchi methods', robust design minimizes the sensitivity of a product or a process to external uncontrolled factors. The present paper expands robust design methods to situations common in electronics design in which design factors interact or in which multiple performance characteristics are to be made robust. This paper proposes a constrained optimization method to achieve robust performance of electronic devices, by limiting attention to only the truly relevant (i.e. feasible) designs that meet all target performance criteria. A recently-solved circuit design problem is revisited to illustrate the proposed method's superior model development, optimization, and robustness-seeking capability.

No abstract received.