• Title/Summary/Keyword: kriging metamodel

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Structural Design of a Container Crane Part-Jaw, Using Metamodels (메타모델을 이용한 크레인 부품 조의 구조설계)

  • Song, Byoung-Cheol;Bang, Il-Kwon;Han, Dong-Seop;Han, Geun-Jo;Lee, Kwon-Hee
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.7 no.3
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    • pp.17-24
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    • 2008
  • Rail clamps are mechanical components installed to fix the container crane to its lower members against wind blast or slip. According to rail clamps should be designed to survive harsh wind loading conditions. In this study, a jaw structure, which is a part of a wedge-typed rail clamp, is optimized with respect to its strength under a severe wind loading condition. According to the classification of structural optimization, the structural optimization of a jaw is included in the category of shape optimization. Conventional structural optimization methods have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome the difficulties, the metamodel using Kriging interpolation method is introduced to replace the true response by an approximate one. This research presents the shape optimization of a jaw using iterative Kriging interpolation models and a simulated annealing algorithm. The new Kriging models are iteratively constructed by refining the former Kriging models. This process is continued until the convergence criteria are satisfied. The optimum results obtained by the suggested method are compared with those obtained by the DOE (design of experiments) and VT (variation technology) methods built in ANSYS WORKBENCH.

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Structural Design for a Jaw Using Metamodels

  • Bang, Il-Kwon;Kang, Dong-Heon;Han, Dong-Seop;Han, Geun-Jo;Lee, Kwon-Hee
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • v.2
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    • pp.329-334
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    • 2006
  • Rail clamps are mechanical components installed to fix the container crane to its bottoms from wind blast or slip. Rail clamps should be designed to survive the harsh wind loading condition. In this study, the jaw structure that is one part of wedge-typed rail clamp is optimized, considering strength under the severe wind loading condition. According to the classification of structural optimization, the structural optimization of a jaw belongs to shape optimization. In the conventional structural optimization methods, they have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome the difficulties, the metamodel using kriging interpolation method is introduced, replacing true response by approximate one. This research presents the shape optimization of a jaw using iterative kriging interpolation models and simulated annealing algorithm. The new kriging models are iteratively constructed by refining the former kriging models. This process is continued until the convergence criteria are satisfied. The optimum results obtained by the suggested method are compared with those obtained by the DOE (design of experiments) and VT (variation technology) methods built in ANSYS WORKBENCH.

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RBDO of Coil Spring Considering Transversal Direction Mode Tracking (횡방향 모드추적을 고려한 코일스프링의 신뢰성기반 최적설계)

  • Lee, Jin Min;Jang, Junyong;Lee, Tae Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.37 no.6
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    • pp.821-826
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    • 2013
  • When the values of design variables change, mode switching can often occur. If the mode of interest is not tracked, the frequencies and modes for design optimization may be miscalculated owing to modes that differ from the intended ones. Thus, mode tracking must be employed to identify the frequencies and modes of interest whenever the values of design variables change during optimization. Furthermore, reliability-based design optimization (RBDO) must be performed for design problems with design variables containing uncertainty. In this research, we perform RBDO considering the mode tracking of a compressive coil spring, i.e., a component of the joint spring that supports a compressor, with design variables containing uncertainty by using only kriging metamodels based on multiple responses approach (MRA) without existing mode tracking methods. The reliability analyses for RBDO are employed using kriging metamodel-based Monte Carlo simulation.

Sensitivity Approach of Sequential Sampling Using Adaptive Distance Criterion (적응거리 조건을 이용한 순차적 실험계획의 민감도법)

  • Jung, Jae-Jun;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.9 s.240
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    • pp.1217-1224
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    • 2005
  • To improve the accuracy of a metamodel, additional sample points can be selected by using a specified criterion, which is often called sequential sampling approach. Sequential sampling approach requires small computational cost compared to one-stage optimal sampling. It is also capable of monitoring the process of metamodeling by means of identifying an important design region for approximation and further refining the fidelity in the region. However, the existing critertia such as mean squared error, entropy and maximin distance essentially depend on the distance between previous selected sample points. Therefore, although sufficient sample points are selected, these sequential sampling strategies cannot guarantee the accuracy of metamodel in the nearby optimum points. This is because criteria of the existing sequential sampling approaches are inefficient to approximate extremum and inflection points of original model. In this research, new sequential sampling approach using the sensitivity of metamodel is proposed to reflect the response. Various functions that can represent a variety of features of engineering problems are used to validate the sensitivity approach. In addition to both root mean squared error and maximum error, the error of metamodel at optimum points is tested to access the superiority of the proposed approach. That is, optimum solutions to minimization of metamodel obtained from the proposed approach are compared with those of true functions. For comparison, both mean squared error approach and maximin distance approach are also examined.

A Structural Design Method Using Ensemble Model of RSM and Kriging (반응표면법과 크리깅의 혼합모델을 이용한 구조설계방법)

  • Kim, Nam-Hee;Lee, Kwon-Hee
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.3
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    • pp.1630-1638
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    • 2015
  • The finite element analysis has become an essential process to investigate the structural performance in many industry fields. In addition, the computer's performance is improving rapidly, but in large design problems, there is a limit to apply the optimal design techniques. For this, it is general to introduce a metamodel based optimization technique. The method to generate an approximate model can be classified into curve fitting and interpolation, and each representative one is response surface model and kriging interpolation method. This study proposes an ensemble model made of RSM and kriging to solve a structural design problem. The suggested method is applied to the designs of two bar and automobile outer tie rod.

Candidate Points and Representative Cross-Validation Approach for Sequential Sampling (후보점과 대표점 교차검증에 의한 순차적 실험계획)

  • Kim, Seung-Won;Jung, Jae-Jun;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.31 no.1 s.256
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    • pp.55-61
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    • 2007
  • Recently simulation model becomes an essential tool for analysis and design of a system but it is often expensive and time consuming as it becomes complicate to achieve reliable results. Therefore, high-fidelity simulation model needs to be replaced by an approximate model, the so-called metamodel. Metamodeling techniques include 3 components of sampling, metamodel and validation. Cross-validation approach has been proposed to provide sequnatially new sample point based on cross-validation error but it is very expensive because cross-validation must be evaluated at each stage. To enhance the cross-validation of metamodel, sequential sampling method using candidate points and representative cross-validation is proposed in this paper. The candidate and representative cross-validation approach of sequential sampling is illustrated for two-dimensional domain. To verify the performance of the suggested sampling technique, we compare the accuracy of the metamodels for various mathematical functions with that obtained by conventional sequential sampling strategies such as maximum distance, mean squared error, and maximum entropy sequential samplings. Through this research we team that the proposed approach is computationally inexpensive and provides good prediction performance.

Thermal conductivity prediction model for compacted bentonites considering temperature variations

  • Yoon, Seok;Kim, Min-Jun;Park, Seunghun;Kim, Geon-Young
    • Nuclear Engineering and Technology
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    • v.53 no.10
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    • pp.3359-3366
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    • 2021
  • An engineered barrier system (EBS) for the deep geological disposal of high-level radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling material, and backfill material. As the buffer fills the empty space between the disposal canisters and the near-field rock mass, heat energy from the canisters is released to the surrounding buffer material. It is vital that this heat energy is rapidly dissipated to the near-field rock mass, and thus the thermal conductivity of the buffer is a key parameter to consider when evaluating the safety of the overall disposal system. Therefore, to take into consideration the sizeable amount of heat being released from such canisters, this study investigated the thermal conductivity of Korean compacted bentonites and its variation within a temperature range of 25 ℃ to 80-90 ℃. As a result, thermal conductivity increased by 5-20% as the temperature increased. Furthermore, temperature had a greater effect under higher degrees of saturation and a lower impact under higher dry densities. This study also conducted a regression analysis with 147 sets of data to estimate the thermal conductivity of the compacted bentonite considering the initial dry density, water content, and variations in temperature. Furthermore, the Kriging method was adopted to establish an uncertainty metamodel of thermal conductivity to verify the regression model. The R2 value of the regression model was 0.925, and the regression model and metamodel showed similar results.

Shape Optimization of a Segment Ball Valve Using Metamodels

  • Lee, Jin-Hwan;Lee, Kwon-Hee
    • Journal of Navigation and Port Research
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    • v.34 no.7
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    • pp.553-558
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    • 2010
  • This study presents the optimization design process of a segment ball valve that involves the reduction of the flow resistance coefficient and the satisfaction of the strength requirement. Numerical analysis of fluid flow and structural analysis have been performed to predict the flow resistance coefficient and the maximum stress of a segment ball valve. In this study, a segment ball valve incorporating the advantages of a ball valve and a butterfly valve has been devised. In general, ball valves are installed in a pipe system where tight shut off is required. Butterfly valves having smaller end-to-end dimension than ball valve can be installed in narrow spaces in a pipe system. The metamodels for the shape design of a segment ball valve are built by the response surface method and the Kriging interpolation model.

Prediction of Consumed Electric Power on a MQL Milling Process using a Kriging Meta-Model (크리깅 메타모델을 이용한 MQL 밀링공정의 소비전력 예측 연구)

  • Jang, Duk-Yong;Jung, Jeehyun;Seok, Jongwon
    • Journal of the Korean Society for Precision Engineering
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    • v.32 no.4
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    • pp.353-358
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    • 2015
  • Energy consumption reduction has become an important key word in manufacturing that can be achieved through the efficient and optimal use of raw materials and natural resources, and minimization of the harmful effects on nature or human society. The successful implementation of this concept can only be possible by considering a product's entire life cycle and even its disposal from the early design stage. To accomplish this idea with milling, minimum quantity lubrication (MQL) strategies and cutting conditions are analyzed through process modeling and experiments. In this study, a model to predict the cutting energy in the milling process is used to find the cutting conditions, which minimize the cutting energy through a Kriging meta-modeling process. The MQL scheme is developed first to reduce the amount of cutting oil and costs used in the cutting process, which is then employed for the entire modeling and experiments.

Minimization of a Cogging Torque for an Interior Permanent Magnet Synchronous Machine using a Novel Hybrid Optimization Algorithm

  • Kim, Il-Woo;Woo, Dong-Kyun;Lim, Dong-Kuk;Jung, Sang-Yong;Lee, Cheol-Gyun;Ro, Jong-Suk;Jung, Hyun-Kyo
    • Journal of Electrical Engineering and Technology
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    • v.9 no.3
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    • pp.859-865
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    • 2014
  • Optimization of an electric machine is mainly a nonlinear multi-modal problem. For the optimization of the multi-modal problem, many function calls are required with much consumption of time. To address this problem, this paper proposes a novel hybrid algorithm in which function calls are less than conventional methods. Specifically, the proposed method uses the kriging metamodel and the fill-blank technique to find an approximated solution in a whole problem region. To increase the convergence speed in local peaks, a parallel gradient assisted simplex method is proposed and combined with the kriging meta-model. The correctness and usefulness of the proposed hybrid algorithm is verified through a mathematical test function and applied into the practical optimization as the cogging torque minimization for an interior permanent magnet synchronous machine.