• Steel and Composite Structures
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• v.45 no.2
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• pp.205-218
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• 2022

Proper calculation of splitting tensile strength (STS) of concrete has been a crucial task, due to the wide use of concrete in the construction sector. Following many recent studies that have proposed various predictive models for this aim, this study suggests and tests the functionality of three hybrid models in predicting the STS from the characteristics of the mixture components including cement compressive strength, cement tensile strength, curing age, the maximum size of the crushed stone, stone powder content, sand fine modulus, water to binder ratio, and the ratio of sand. A multi-layer perceptron (MLP) neural network incorporates invasive weed optimization (IWO), cuttlefish optimization algorithm (CFOA), and electrostatic discharge algorithm (ESDA) which are among the newest optimization techniques. A dataset from the earlier literature is used for exploring and extrapolating the STS behavior. The results acquired from several accuracy criteria demonstrated a nice learning capability for all three hybrid models viz. IWO-MLP, CFOA-MLP, and ESDA-MLP. Also in the prediction phase, the prediction products were in a promising agreement (above 88%) with experimental results. However, a comparative look revealed the ESDA-MLP as the most accurate predictor. Considering mean absolute percentage error (MAPE) index, the error of ESDA-MLP was 9.05%, while the corresponding value for IWO-MLP and CFOA-MLP was 9.17 and 13.97%, respectively. Since the combination of MLP and ESDA can be an effective tool for optimizing the concrete mixture toward a desirable STS, the last part of this study is dedicated to extracting a predictive formula from this model.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.109-118
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• 2005

The parameter design is the most emphasized measure by researchers for a new products development. It is critical for makers to achieve simultaneously in both the time-to-market production and the quality enhancement. However, there are difficulties in practical application, such as (1) complexity and nonlinear relationships co-existed among the system's inputs, outputs and control parameters, (2) interactions occurred among parameters, (3) where the adjustment factors of Taguchi's two-phase optimization procedure cannot be sure to exist in practice, and (4) for some reasons, the data became lost or were never available. For these incomplete data, the Taguchi methods cannot treat them well. Neural networks have a learning capability of fault tolerance and model free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful fields include diagnostics, robotics, scheduling, decision-making, prediction, etc. This research is a case study of spherical annealing model. In the beginning, an original model is used to pre-fix a model of parameter design. Then neural networks are introduced to achieve another model. Study results showed both of them could perform the highest spherical level of quality.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.3
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• pp.89-97
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• 1997

A genetic algorithm (GA) is a stochastic direct search strategy that mimics the process of genetic evolution. The GA applied herein works on a population of structural designs at any one time, and uses a structured information exchange based on the principles of natural selection and wurvival of the fittest to recombine the most desirable features of the designs over a sequence of generations until the process converges to a "maximum fitness" design. Principles of genetics are adapted into a search procedure for structural optimization. The methods consist of three genetics operations mainly named selection, cross- over and mutation. In this study, a method of finding the optimum topology of truss/beam structure is pro- posed by using the GA. In order to use GA in the optimum topology problem, chromosomes to FEM elements are assigned, and a penalty function is used to include constraints into fitness function. The results show that the GA has the potential to be an effective tool for the optimal design of structures accounting for sizing, geometrical and topological variables.variables.

• Tribology and Lubricants
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• v.21 no.4
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• pp.185-192
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• 2005

The press cutter is productive equipment that practically manufactures mechanical components and polymer-based materials such as fabrics, papers, films, leathers, and rubbers into the desired shapes using a press cutting tool. The plate cutting process is one of the primary energy absorbing mechanisms in a grounding or collision event between a press cutter and a material on a die. The cutting mechanism is complicated and involves plastic flows of a plate in the vicinity of the tip, friction between the wedge and the plate, deformation of the plate. In this paper, we studied the effect of friction between cutter and plastic sheet far producing precise and superior products. In this paper, the press cutter is analyzed numerically using MARC finite element program for a optimization design of a press cutter. The FEM computed results show that the maximum von Mises stress is concentrated on the tip of a press cutter, which may lead to the edge wear or impact wear of the sharp cutter. Based on the FEM result and Taguchi's experimental design method, the optimized design model 9 for a press cutter is recommended as a best one.

• Structural Engineering and Mechanics
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• v.32 no.5
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• pp.667-683
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• 2009

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead's Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.205-210
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• 2006

Nowaday manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. It is measured automatically do much quality characteristics thereby and great many data happen in a day. corporations is important if have gotten fast information that are useful from wide data to go first in international competition according to these change. Statistical process control(SPC) techniques are used as a problem solution tool at manufacturing process until present. However, this statistical methods is not applied more extensively because have much restrictions in realistic problem. In this paper, wish to develop more realistic and scientific new statistical design techniques doing to integrate data mining(DM) and statistical methods by the alternative to cope these problem. First step selects significant factor using DM techniques from datas of manufacturing process including much factors and second step wish to find optimum of process after get the estimated response function through response surf ace methodology(RSM) that is statistical techniques.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1165-1173
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• 2008

An water-reuse network design has drawn attention as a systematic method of reducing fresh water usage and increasing water-using efficiency. The final goal of an eco-industrial park(EIP) is accomplishing industrial sustainability and constructing water-reuse network can be a solution. When designing water-reuse network connecting various processes which consume water, the water pinch technology can be used frequently, since it simultaneously minimize freshwater usage and wastewater discharge. In this research water pinch technology is applied to develop an effective water-reuse network in an EIP. Three scenarios based on different reusing strategies were developed. The results show that the final water-reuse network can reduce the total fresh water usage more than 30%, while the water expenses decrease by 20%. It can be concluded that water pinch technology is an effective tool to optimize water-reuse network among different industrial facilities.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.122-131
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• 2010

In this paper, we design and implement CTOC, a new bytecode analysis and translation tool. We also propose E-Tree, a new intermediate code, to efficiently deal with intermediate codes translated from bytecodes. E-Tree is expressed in a tree form by combining relevant bytecode instructions in basic blocks of eCFG to overcome the weaknesses of bytecodes such as complexity and analytical difficulty. To demonstrate the usefulness and possible extensibility of CTOC, we show the creation process of eCFG and E-Tree through practical bytecode analysis and translation and shows the optimization process of a bytecode program as an example of possible extensibility.

• Transactions of Materials Processing
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• v.18 no.5
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• pp.392-400
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• 2009

Clinching is a method of joining sheet metals together. This process can be substituted for the resistance spot welding on the joining of aluminum alloys. However, the joining strength of the clinching is lower than that of welding and riveting. The objective of this paper is to evaluate the effect of shape parameters of tools on the joining strength of the clinching and to optimize clinching tools. Twelve parameters have been selected as shape parameters on the clinching tools such as punch and die. The design of experiments (DOE) method is employed to investigate the effect of the shape parameters of tools on the joining strength of the clinching. The neck thickness and undercut of the clinched sheet metal after the clinching, and the separation load at detaching are estimated from the result of FEA using DEFORM. Optimal combination of shape parameters to maximize the joining strength of clinching is determined on the basis of the result of DOE and FEA. In order to validate the result of DOE and FEA, the experiment of clinching is performed for the optimal combination of shape parameters. It is shown from the result of the experiment that optimization of shape parameters improves the joining strength of clinching.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.33-39
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• 2006

These days manufacture technology and manufacture environment are changing rapidly. By development of computer and enlargement of technique, most of manufacture field are computerized. In order to win international competition, it is important for companies how fast get the useful information from vast data. Statistical process control(SPC) techniques have been used as a problem solution tool at manufacturing process until present. However, these statistical methods are not applied more extensively because it has much restrictions in realistic problems. These statistical techniques have lots of problems when much data and factors are analyzed. In this paper, we proposed more practical and efficient a new statistical design technique which integrated data mining (DM) and statistical methods as alternative of problems. First step is selecting significant factor using DM feature selection algorithm from data of manufacturing process including many factors. Second step is finding optimum of process after estimating response function through response surface methodology(RSM) that is a statistical techniques