• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.1
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• pp.44-50
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• 2015

This paper presents a heuristic process-optimization procedure for minimizing warpage in injection-molded parts based on the dynamic robust design methodology. The injection molding process is known to have intrinsic variations of its process conditions due to various factors, including incomplete process control facilities. The aim of the robust design methodology advocated by Taguchi is to determine the optimum design variables in a system which is robust to variations in uncontrollable factors. The proposed procedure can determine the optimal robust conditions of injection molding processes at a minimum cost through a trade-off strategy between the degree of warpage and the packing time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.215-224
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• 2000

In order to reduce the variation effects of uncertainties in the engineering environments, new robust optimization method, which considers the uncertainties in design process, is proposed. Both design variables and system parameters are considered as random variables about their nominal values. To ensure the robustness of performance function, a new objective is set to minimize the variance of that function. Constraint variations are handled by introducing probability constraints. Probability constraints are solved by the advanced first order second moment (AFOSM) method based on the reliability theory. The proposed robust optimization method has an advantage that the second derivatives of the constraints are not required. The suggested method is examined by solving three examples and the results are compared with those for deterministic case and those available in literature.

• Journal of Integrative Natural Science
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• v.13 no.3
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• pp.92-96
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• 2020

Taguchi's robust parameter design is an approach to reduce the performance variation of quality characteristics in products and processes. In robust design, the signal-to-noise ratio (SN ratio) was used to find the optimum condition to minimize the variation of quality characteristics as much as possible and bring the average of quality characteristics closer to the target value. In this paper, we propose a simultaneous optimization method based on a linear model of the SN ratio as a method to find the optimal condition of the control factor in case of multi-characteristics. In addition, the proposed method and the existing method were compared and studied by taking actual cases.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.157-165
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• 1995

The new formulation of designing the two degree of freedom(TDF) robust controller is proposed using $H_{\infty}$optimization and model matching method. In this formulation the feedback controller and feedforward controller are designed in a single step using $H_{\infty}$optimization procedure. Roughly speaking, the feedback controller is designed to meet robust stability and disturbance rejection specifications, while the feedforward controller is used to improve the robust model matching properties of the closed loop system. The proposed formulation will be illustrated and evaluated on a seeker scan-loop. And the performances of TDF robust controller are compared with those of the $H_{\infty}$ controller designed using Loop Shaping Design Procedure proposed by McFarlane and Glover.lover.

• Structural Engineering and Mechanics
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• v.76 no.4
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• pp.529-540
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• 2020

Concurrent topology optimization of macrostructure and microstructure has attracted significant interest due to its high structural performance. However, most of the existing works are carried out under deterministic conditions, the obtained design may be vulnerable or even cause catastrophic failure when the load position exists uncertainty. Therefore, it is necessary to take load position uncertainty into consideration in structural design. This paper presents a computational method for robust concurrent topology optimization with consideration of load position uncertainty. The weighted sum of the mean and standard deviation of the structural compliance is defined as the objective function with constraints are imposed to both macro- and micro-scale structure volume fractions. The Bivariate Dimension Reduction method and Gauss-type quadrature (BDRGQ) are used to quantify and propagate load uncertainty to calculate the objective function. The effective properties of microstructure are evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The bi-directional evolutionary structural optimization (BESO) method is used to obtain the black-and-white designs. Several 2D and 3D examples are presented to validate the effectiveness of the proposed robust concurrent topology optimization method.

• Structural Engineering and Mechanics
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• v.53 no.6
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• pp.1127-1141
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• 2015

The optimum design of liquid column dampers in seismic vibration control considering system parameter uncertainty is usually performed by minimizing the unconditional response of a structure without any consideration to the variation of damper performance due to uncertainty. However, the system so designed may be sensitive to the variations of input system parameters due to uncertainty. The present study is concerned with robust design optimization (RDO) of liquid column vibration absorber (LCVA) considering random system parameters characterizing the primary structure and ground motion model. The RDO is obtained by minimizing the weighted sum of the mean value of the root mean square displacement of the primary structure as well as its standard deviation. A numerical study elucidates the importance of the RDO procedure for design of LCVA system by comparing the RDO results with the results obtained by the conventional stochastic structural optimization procedure and the unconditional response based optimization.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.255-261
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• 2005

Many designed experiments require the simultaneous optimization of multiple responses. In this paper, we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.11
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• pp.2825-2836
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• 1994

The engineering optimization has been developed for the automatic design of engineering systems. Since the conventional optimum is determined without considering noise factors, applications to practical problems can be limited. Current design practice tends to account for these noises by the specification of closer tolerances or the use of safety factors. However, these approaches may be very expensive. Thus the consideration on the noises of design variables is needed for optimal design. A method is presented to find robust solutions for unconstrained optimization problems. The method is applied to discrete and continuous variables. The orthogonal array is utilized based on the Taguchi concept. Through mathematical proofs and numerical examples, it is verified that solutions from the suggested method are more insensitive than the conventional optimum within the range of variations for design variables.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.267-280
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• 2022

Multiscale structure has attracted significant interest due to its high stiffness/strength to weight ratios and multifunctional performance. However, most of the existing concurrent topology optimization works are carried out under deterministic load conditions. Hence, this paper proposes a robust concurrent topology optimization method based on the bidirectional evolutionary structural optimization (BESO) method for the design of structures composed of periodic microstructures subjected to uncertain dynamic loads. The robust objective function is defined as the weighted sum of the mean and standard deviation of the module of dynamic structural compliance with constraints are imposed to both macro- and microscale structure volume fractions. The polynomial chaos expansion (PCE) method is used to quantify and propagate load uncertainty to evaluate the objective function. The effective properties of microstructure is evaluated by the numerical homogenization method. To release the computation burden, the decoupled sensitivity analysis method is proposed for microscale design variables. The proposed method is a non-intrusive method, and it can be conveniently extended to many topology optimization problems with other distributions. Several numerical examples are used to validate the effectiveness of the proposed robust concurrent topology optimization method.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.