• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.117-134
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• 2004

This study suggests a data driven optimization approach, which simulates the models of human learning processes from cognitive sciences. It shows how the human learning processes can be simulated and applied to solving combinatorial optimization problems. The main advantage of using this method is in applying it into problems, which are very difficult to simulate. 'Undecidable' problems are considered as best possible application areas for this suggested approach. The concept of an 'undecidable' problem is redefined. The learning models in human learning and decision-making related to combinatorial optimization in cognitive and neural sciences are designed, simulated, and implemented to solve an optimization problem. We call this approach 'SLO : simulated learning for optimization.' Two different versions of SLO have been designed: SLO with position & link matrix, and SLO with decomposition algorithm. The methods are tested for traveling salespersons problems to show how these approaches derive new solution empirically. The tests show that simulated learning for optimization produces new solutions with better performance empirically. Its performance, compared to other hill-climbing type methods, is relatively good.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.277-280
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• 2005

A shape optimization is applied to achieve a design objective in three-dimensional forging processes. In multi-stage forging processes, among the important design aspects, the die shape fur preforming is regarded as the design variable since it influences the forged part relatively higher than the others. The rigid-plastic finite element method and the sensitivity method are employed and formulated to solve a formulated optimization problem. An approximation scheme is also used for the direction search during the optimization. The upset forging of a square box is selected as a test example in order to demonstrate and verify the optimization process of this study. After the optimization, the optimized shape of the die yields a finial product of desire shape.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.452-460
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• 2001

Most of researches of tolerance optimization have used a simple sum of tolerance-cost functions with several constraint equations as an optimization model. However, if there is a machining sequence with more than one processes to complete a part, and machining failure, i.e., out-of-tolerance occurs at one of the intermediate processes, the tolerance-cost of this process should be added by the machining cost of all the previous processes already completed on the part. In this study, an accumulated scrap cost model(ASCM) is proposed considering the scrapped machining cost, and applied to a simple assembly example. The result of tolerance optimization using ASCM is compared with that of using a traditional optimization model to confirm its effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.36-39
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• 1996

In chemical processes, measurement errors reduce the credibility of information and cause inconsistency in material and energy balances. Because multicomposition flows and temperature measurements make material and energy balances nonlinear equations, data reconciliation becomes a nonlinear constrained optimization problem. In multicomposition processes, if we follow general optimization procedure, the number of measurement variables is so large that data reconciliation requires much computation time. We propose the decomposition procedure to reduce the computation time without the decrease of accuracy of data reconciliation. Decomposition procedure finds global variables, that can reduce the nonlinearity of constraints, and divides two sub-optimization problems. Once we optimize the global variables at upper level, we can easily optimize the remain variables at tower level, We can obtain the short computational time and the same accuracy as SQP optimization method.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.2
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• pp.45-50
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• 2021

In the recent fourth industrial revolution, the demand for additive processes has emerged rapidly in many mechanical industries, including the aircraft and automobile industries. Additive processes, in contrast to subtractive processes, can be used to produce complex-shaped products, such as three-dimensional cooling systems and aircraft parts that are difficult to produce using conventional production technologies. However, the limitations of additive processes include nonuniform surface quality, which necessitates the use of post-processing techniques such as subtractive methods and grinding. This has led to the need for hybrid machines that combine additive and subtractive processes. A hybrid machine uses additional additive and subtractive modules, so product deformation, for instance, deflection, is likely to occur. Therefore, structural analysis and design optimization of hybrid machines are essential because these defects cause multiple problems, such as reduced workpiece precision during processing. In this study, structural analysis was conducted before the development of an additive/subtractive hybrid processing machine. In addition, structural optimization was performed to improve the stability of the hybrid machine.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.12
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• pp.2101-2109
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• 2003

Structural shape optimization offers engineers with numerous advantages in designing shapes of structures. However, excessive relocation of nodes often cause distortion of elements and eventually result in degrade of accuracy and even halts of processes. To overcome these problems, an effective method, Selective Element Method(SEM), has been developed. This paper describes the basic concept of SEM and processes to implement into real-world problem. 2-D and 3-D shape optimization problems have been chosen to show the performance of the method. Though some limitations have been found, it was concluded that SEM can be useful in general shape optimization and even in some special cases such as decision of optimal weld line location.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.395-395
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• 2000

Optimization of existing processes becomes more important than the past as environmental problems and concerns about energy savings stand out. When we can model a process mathematically, we can easily optimize it by using the model as constraints. However, modeling is very difficult for most chemical processes as they include numerous units together with their correlation and we can hardly obtain parameters. Therefore, optimization that is based on the process models is, in turn, hard to perform. Especially, f3r unknown processes, such as bioprocess or microelectronics materials process, optimization using mathematical model (first principle model) is nearly impossible, as we cannot understand the inside mechanism. Consequently, we propose a few optimization method using empirical model evolutionarily instead of mathematical model. In this method, firstly, designing experiments is executed fur removing unecessary experiments. D-optimal DOE is the most developed one among DOEs. It calculates design points so as to minimize the parameters variances of empirical model. Experiments must be performed in order to see the causation between input variables and output variables as only correlation structure can be detected in historical data. And then, using data generated by experiments, empirical model, i.e. response surface is built by PLS or MLR. Now, as process model is constructed, it is used as objective function for optimization. As the optimum point is a local one. above procedures are repeated while moving to a new experiment region fur finding the global optimum point. As a result of application to the pulp digester benchmark model, kappa number that is an indication fur impurity contents decreased to very low value, 3.0394 from 29.7091. From the result, we can see that the proposed methodology has sufficient good performance fur optimization, and is also applicable to real processes.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.05a
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• pp.19-22
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• 2004

A scheme of shape optimization by new approximation approach is applied to design of a flow guide in three-dimensional extrusion processes. The optimization scheme is presented to reduce computation time fur the optimization process and applied to an H-section extrusion problem for verifying the efficiency and the usefulness. The object of optimization is to minimize the deviation of exit velocity and control points of a Bezier curve describing the shape of the flow guide are regarded as design variables. The effectiveness of the proposed scheme is then demonstrated through the applied example.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.05a
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• pp.262-265
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• 2005

A shape optimization of flow guide is accomplished to minimize the wear rate of die in three-dimensional flat-die extrusion processes. In order to achieve the balanced flow and the uniformed distribution of the effective strain during the extrusion, a multi-objective optimization is implemented. During the process of optimization formulation, the flow balance and the deviation of strain is considered as constrained conditions. The proposed approach is applied to an extrusion of H section. Through the optimization, it has been confirmed that the wear rate of die can be minimized satisfying the constraint.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.371-374
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• 2005

We accomplished optimization for pump and treat (P&T) designs in consideration of degradation processes such as retardation and biodegradation, which are significant for contaminant fate in hydrogeology. For more desirable remediation, optimal pumping duration and minimum pumping rate constraint problems are studied. After a specific P&T duration, it replaces the P&T with the enhanced natural attenuation (ENA), which induces aerobic biodegradation by maintaining oxygen concentration. The design in this strategy carries out the optimization for the number and locations of oxygen injection wells.