• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.649-659
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• 2019

Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using Moth-Flame Optimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.

• International journal of steel structures
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• v.18 no.5
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• pp.1598-1606
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• 2018

An integrated optimal structural design method for a diagrid structure and control device was developed. A multi-objective genetic algorithm was used and a 60-story diagrid building structure was developed as an example structure. Artificial wind and earthquake loads were generated to assess the wind-induced and seismic responses. A smart tuned mass damper (TMD) was used as a structural control system and an MR (magnetorheological) damper was employed to develop a smart TMD (STMD). The multi-objective genetic algorithm used five objectives including a reduction of the dynamic responses, additional stiffness and damping, mass of STMD, capacity of the MR damper for the integrated optimization of a diagrid structure and a STMD. From the proposed method, integrated optimal designs for the diagrid structure and STMD were obtained. The numerical simulation also showed that the STMD provided good control performance for reducing the wind-induced and seismic responses of a tall diagrid building structure.

• Transactions of Materials Processing
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• v.30 no.1
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• pp.27-34
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• 2021

In this study, response surface method (RSM) was used in modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goals of optimization were the maximum forming angle, minimum thickness reduction, and minimum surface roughness, with varying values in response to changes in production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model for modeling the variations in the forming angle, thickness reduction, and surface roughness in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process based on experimental design. The results showed that RSM can be effectively used to control the forming angle, thickness reduction, and surface roughness.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.687-696
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• 2007

Preference ordering approach is applied to optimize the parameters of Tank model using multi-objective genetic algorithm (MOGA). As more than three multi-objective functions are used in MOGA, too many non-dominated optimal solutions would be obtained thus the stakeholder hardly find the best optimal solution. In order to overcome this shortcomings of MOGA, preference ordering method is employed. The number of multi-objective functions in this study is 4 and a single Pareto-optimal solution, which is 2nd order efficiency and 3 degrees preference ordering, is chosen as the most preferred optimal solution. The comparison results among those from Powell method and SGA (simple genetic algorithm), which are single-objective function optimization, and NSGA-II, multi-objective optimization, show that the result from NSGA-II could be reasonalby accepted since the performance of NSGA-II is not deteriorated even though it is applied to the verification period which is totally different from the calibration period for parameter estimation.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.614-622
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• 2018

Based on army operating road profile, the endurance test of military vehicle aims to reproduce the similar loading conditions with mixture of proving ground tracks. It is so called as endurance test mode and its optimal generation is important to meet high reliability of endurance test. In this paper, proving ground optimization is proposed to achieve a close match to the target profile. Several performance measures such as torque-revolution counts or transmission ratio for the powertrain system can be considered as one of the objective functions. However, the one-side optimal endurance test mode may give the poor solution in the whole system point of view. To incorporate several goals simultaneously, this paper employs multi-objective optimization technique to generate endurance test mode. One of the most widely used method, weighted-sum method is applied here and the case study is discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.380-383
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• 2003

In this paper, we propose a fuzzy group decision making method for multiple decision maker-multiple objective programming problems to obtain the agreeable solution. In the proposed method, considering the vague nature of human subjective judgement it is assumed that each of multiple decision makers has a fuzzy goal for each of his/her own objective functions. After eliciting the membership functions from the decision makers for their fuzzy goals, total M-Pareto optimal solution concept is defined in membership spaces in order to deal with multiple decision maker-multiple objective programming problems. For generating a candidate of the agreeable solution which is total M-Pareto optimal, the extended weighted minimax problem is formulated and solved for some weighting vector which is specified by the decision makers in their subjective manner, Given the total M-Pareto optimal solution, each of the derision makers must either be satisfied with the current values of the membership functions, or update his/her weighting vector, However, in general, it seems to be very difficult to find the agreeable solution with which all of the decision makers are satisfied perfectly because of the conflicts between their membership functions. In the proposed method, each of the decision makers is requested to estimate the degree of satisfaction for the candidate of the agreeable solution. Using the estimated values or satisfaction of each of the decision makers, the core concept is desnfied, which is a set of undominated candidates. The interactive algorithm is developed to obtain the agreeable solution which satisfies core conditions.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.803-808
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• 2002

A computer-aided alloy-designing technique to develop the insert metal for transient liquid phase (TLP) bonding was applied to high aluminum Ni-base superalloys. The main procedure of a mathematical programming method was to obtain the optimal chemical composition through rationally compromising the plural objective performances of insert metal by a grid-search which involved data estimation from the limited experimental data using interpolation method. The objective function Z which was introduced as an index of bonding performance of insert metal involved the melting point, hardness (strength), formability of brittle phases and void ratio (bonding defects) in bond layer as the evaluating factors. The contour maps of objective function Z were also obtained applying the interpolation method. The compositions of Ni-3.0%Cr-4.0%B-0.5%Ce (for ${\gamma}$/${\gamma}$/${\beta}$ type alloy) and Ni3.5%Cr-3.5%B-3%Ti (for ${\gamma}$/${\gamma}$ type alloy) which optimized the objective function were determined as insert metal. SEM observations revealed that the microstructure in bond layers using the newly developed insert metals indicated quite sound morphologies without forming microconstituents and voids. The creep rupture properties of both joints were much improved compared to a commercial insert metal of MBF-80 (Ni-15.5%Cr-3.7%B), and were fairly comparable to those of base metals.

• Journal of the Ergonomics Society of Korea
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• v.32 no.1
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• pp.47-58
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• 2013

Objective: The aim of this study is to review the job stress measurement for applying in nuclear power plants(NPPs). Background: The standard and guideline to evaluate and manage the job stress is insufficient in NPPs. Although job stress might have a negative effect on task performance particularly it can be related with human error in NPPs. Method/Results: This paper considered the objective and subjective stress measurements. One of the questionnaire(Korean Occupational Stress Scale) and the experiment method was investigated to apply in NPPs. KOSS was analyzed about the inter item consistency and correlation with the workload, and relative importance. In the objective evaluation considered the experiment method for the physical and mental job stress and analyzed from the phased point of view. Conclusion/Application: The measurement and criteria to evaluate job stress for operators must be complemented on the job characters and environments in NPPs. This study may support to confirm and manage the job stress in NPPs. The study of more specific methodology on job stress in NPPs is required on the basis of this paper.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.4
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• pp.16-23
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• 2012

At present, the significance of a new manufacturing system that can shift from 'mass production' and consider life cycles of a product is pointed out and extremely expected. In such a situation, it is recognized that the modular design, often called 'unit design,' is the important design methodology which realizes the new production system enabling 'cost reduction,' 'flexible production of a multi-functional artifact,' 'settlement of an environmental issue,' and so on. A module (unit) of a product is generally defined as 'the parts group made into the sub-system from a certain specific viewpoint.' So far, there have been many researches related to the modular design. However, they are often limited to a certain viewpoint (objective). This paper proposes a simple but effective method for multi-objective modular design. In the proposed method, a new design metric, called similarity index, is proposed to evaluate the modular design candidates from the multiple viewpoints.